Stochastic Resource Allocation for Energy-Constrained Systems
© D. G. Sachs and D. L. Jones. 2009
Received: 21 December 2008
Accepted: 5 June 2009
Published: 14 July 2009
Battery-powered wireless systems running media applications have tight constraints on energy, CPU, and network capacity, and therefore require the careful allocation of these limited resources to maximize the system's performance while avoiding resource overruns. Usually, resource-allocation problems are solved using standard knapsack-solving techniques. However, when allocating conservable resources like energy (which unlike CPU and network remain available for later use if they are not used immediately) knapsack solutions suffer from excessive computational complexity, leading to the use of suboptimal heuristics. We show that use of Lagrangian optimization provides a fast, elegant, and, for convex problems, optimal solution to the allocation of energy across applications as they enter and leave the system, even if the exact sequence and timing of their entrances and exits is not known. This permits significant increases in achieved utility compared to heuristics in common use. As our framework requires only a stochastic description of future workloads, and not a full schedule, we also significantly expand the scope of systems that can be optimized.
The goal of resource allocation is to assign a system's resources to applications in the way that maximizes the system's utility to the user. Resource allocation is in general a difficult problem, and as a result the allocation of resources to multiple applications in a multimedia system has seen considerable research. Ideally, we would be able to allocate resources—CPU time, network bandwidth, energy—in a way that best reflects the user's needs and hence maximizes the utility achieved by the user.
We specifically consider the case of a mobile system that performs several simultaneous tasks, each of which can be reconfigured in several modes with a variety of different CPU and network utilizations. Each of the modes is associated with a specific utility value that represents the quality of service in some way that is meaningful to the user.
In the traditional problem setup, allocation is done by assuming that the same tasks will run from startup until a specified future time . If this is the case, the energy constraint and runtime constraint can be converted into a single constraint on power consumption, and the allocation problem can then be converted into an ordinary knapsack problem, subject to the constraints on network load, CPU load, and system power. Although the resulting allocation problem is an NP-hard knapsack problem, it is usually small enough to be computationally tractable and also tends to be amenable to fast heuristic solutions [2, 3].
In the context of unvarying workloads, the available energy and requested runtime are converted into a power constraint, and the resulting allocation is optimal if the system is allowed to run until the battery is exhausted. However, the runtime may be longer than requested due to the discrete selection of available application configurations.
The allocation problem where all workloads vary, but are all known in advance, can be solved using a straightforward extension of the techniques used for a single workload: the conversion to a knapsack problem by simultaneously considering all sets of applications. In this form, the energy constraint would be left unconverted, and the knapsack problem would optimize utility over all sets of applications that run during the battey's lifetime. In this case, there is a set of four constraints: CPU time, network bandwidth, desired runtime, and total energy available. However, this optimization problem is particularly complex as it requires the evaluation of a cross product of all configurations for each set of applications that will run during the entire running time of the system.
Due to the complexity of this optimization problem, various simplifications have been proposed. One approach to solve the energy-allocation problem involves creating a list of applications that will run in the future, and allocating energy to each of these applications in priority order . Although this greedy heuristic can approach optimality if applications are being admitted only and do not have variable utility, it does not support applications with multiple possible utility levels and does not always have a clear ordering when multiple resources are considered. Another approach involves the use of various suboptimal optimization strategies, including the integer programming techniques described by Lee  and a fast heuristic solution to the underlying multidimensional multichoice knapsack described by Moser .
There is also an important class of problems in this family that have not been addressed in the literature; cases where the workload schedule is not known in advance, and instead only a probability distribution of workloads is known.
The theory of Lagrangian optimization  offers a framework in which we can optimally allocate resources under relatively weak convexity assumptions, and hence provides an approach we can take to solve this entire class of allocation problems without needing to solve a full NP-hard optimization problem. In addition to providing a direct solution to the scheduling of multiple workloads known in advance, the Lagrangian approach also allows us to solve the allocation problem stochastically, permitting statisically optimal allocations to be made even if we have only a probability distribution of the workloads that are to be run. In other words, through the use of the Lagrangian framework, we must know only what might run, and not necessarily if or when.
2. Stochastic Allocation Problem
Consider a battery-powered wireless security monitor system consisting of a controller and multiple cameras, placed to monitor an area for specified period of time. For much of the time, the area it is monitoring shows nothing of interest, and the utility gained by providing a high-quality representation of the area is low. Sometimes we may know when interesting events (such as a person appearing within the view of the camera) will occur; for example, when the facility being monitored opens and closes. But also consider the case where, while , for example, we may know from prior experience that of the time, events of interest will occur, we do not know in advance exactly when these events will occur or which camera or cameras will see them. In either case, it is important that all of the cameras operate for the entire requested interval, and that when events of interest occur we get high-quality video from all of the cameras that have views of the events. This setup describes a stochastic allocation problem, which we analyze as a variant of the prescheduled workload problem described in . Unlike the setup in , instead of having a list of workloads and the time periods that they are active, we have a list of potential or representative workloads, and probabilities that they are active at any given time instant.
This setup describes a stochastic allocation problem that differs from problems previously solved in the literature in that not only are applications entering and exiting the system, but they are doing so in an unscheduled and unpredictable way. We know in advance only that the tasks may appear with a certain probability, not when or even if they will.
2.1. Utility Model
The utility model we use is that each application configuration is assigned a "utility rate" that is additive across applications and time. In other words, if we select a particular application configuration, we credit the system with its corresponding utility for as long as that configuration is active. We further assume that the utility of the application configurations increases as the resource utilization increases, and that the utility/energy curve of the applications being optimized is convex or nearly so, which implies a diminishing return in utility as more energy is expended.
For our experiments, we assigned increasing utility rates to application configurations as the frame rate and number of quantizer steps are increased. However, our utility model is general enough to permit the replacement of these assigned utilities with values that better reflect the actual utility of the tasks, for example, by assigning utility based on perceptual values of video quality derived from human trials.
2.2. Problem Formulation
Inputs to this optimization problem are an application list and the state of the system. Each application entry includes data providing estimates of the CPU utilization, network utilization, and utility associated with each configuration for the application. Network and CPU constraints are implemented using these utilization values, which represent a fraction of the total network and CPU time available used for a particular application. To avoid overloading the resource, the total utilization of both the CPU and the network must be less than or equal to 1.
Each application is represented with a unique ID ; represents the CPU operating frequency for a particular application, and is the selected configuration ID for the application. Applications entering and leaving the system result in a switch to a new workload. The utility of a particular workload is equal to the sum of the selected configurations of all applications running in a particular workload. The objective of the optimization is to maximize the integral of the sum of instant utilities of all running applications over the desired system runtime, such that at no point the CPU and network bounds are exceeded and the total energy consumed over the specified runtime is less than or equal to the starting battery energy .
Note that in this context, the energy constraint, and hence the average power constraint, is special because it extends across all workloads in the system. If a particular workload uses less energy than the average, another workload can use more. This is because energy that is not used is conserved for later use. The conservability of energy means that energy and power must be treated differently when solving the optimization problem, and it causes a dependency across workloads when finding optimal application configurations.
2.3. Naive Solution
Because the average power is added across different workloads in the stochastic allocation problem above, the allocation algorithm is equivalent to solving one instance of the NP-hard multidimensional multichoice knapsack problem (formally defined in ) which optimizes over every application and workload, choosing exactly one configuration for each application in every workload. Although the CPU and network constraint is affected only by application configurations selected in the current workload, the average power constraint depends on the configurations chosen for all applications in every workload that may potentially execute on the system. This means that the knapsack optimizer must evaluate all configurations for all applications across all workloads, which rapidly becomes computationally prohibitive as the number of applications and potential workloads increases. Suboptimal solutions such as the approximation algorithm presented in  can reduce the required computation, but even the use of these suboptimal approximations still leaves a large computationally complex problem.
3. Lagrangian Optimization
Solving a constrained problem is generally difficult. Specifically, the direct solution to a constrained optimization problem described in the previous section is equivalent to solving an NP-hard knapsack, where the knapsack represents the energy contained in the battery and the items that can be placed in the knapsack represent the various configurations of the tasks that run during the lifetime of the battery. To make matters worse, because tasks can enter and leave the system, it is not optimal to simply optimize for the current workload (as is done in ); if low-utility tasks enter the system early, they will "soak up" more than their share of energy from the battery, leaving little energy for high-utility tasks that arrive later. As a result, we cannot simply optimize for the tasks that are available now; we must optimize over the entire schedule of tasks that will arrive between the system's startup time and the time the battery is exhausted.
Because the underlying knapsack problem is nonpolynomial in complexity, this is a combinatorial explosion. To optimize over two different workloads that appear at different times, we must (in the worst case) evaluate every combination of application configurations in the first workload and every combination of application configurations in the second workload pairwise. In other words, the computational time required increases exponentially as the number of different workloads increases.
Because the combinatorial explosion that results when we must jointly optimize across varying workloads, we wanted to find a way to optimize the performance of a battery-operated system while keeping the optimization "local" to a particular workload and therefore tractable. One tool that can be used to do this is Lagrangian optimization.
3.1. Lagrangian Construction
Instead of having the constraint inside a maximization operator, we have only a linear combination of the utility analog and the constrained functions . The constraint has been removed; it will be used to pick a particular value of but does not directly affect the minimization.
In this construction, the Lagrange multiplier represents a particular tradeoff between the term being maximized and the constrained term . This formulation can in fact be generalized to an arbitrary number of constraints by introducing a separate Lagrange multiplier for each constraint to be eliminated.
3.2. Lagrangian Optimization of Independent Cells
3.3. Optimality of the Lagrangian Formulation
Before the Lagrangian reformulation is used to solve an optimization problem, it is important to understand when and why it is equivalent to directly solving the original constrained optimization problem. This equivalence was shown for the general case with multiple Lagrange multipliers by Everett ; for clarity I summarize his argument for the multiple-cell, single- case of (7) here.
and therefore satisfies the original optimization problem. In other words, if we solve the reformulated problem for some and get back a configuration for which is , for that particular and the solutions of the constrained and unconstrained optimization problems are identical.
Although we have proven that any solution found using the unconstrained Lagrangian form is in fact a solution to the original constrained optimization problem, we have not proven that we can find a solution corresponding to a particular value for . In fact, not all values of that can be reached with equality in the constrained form of the optimization can be achieved in the Lagrangian form; specifically, a particular value for can be "found" by the Lagrangian optimization if it lies on a convex portion of the payoff verses resource use curve . In other words, if a scatter plot is built using the resource consumption on the x-axis and the payoff on the y-axis for all possible configuration sets , the Lagrangian optimizer will be able to match any values of that correspond to points on the convex hull of this scatter plot.
If the desired value of does not correspond to a point on the convex hull of the resource-payoff scatter plot, when we search for an appropriate value of , we will locate the value of that selects the point on the convex hull that comes closest to consuming the desired amount of the resource. This selection is still "optimal" in the sense that no other configuration achieves a greater payoff for the same or lesser resource utilization; however, selection of another point could result in a higher total payoff by using more of the available resource.
The convexity property of the Lagrangian optimization can also be used to create a fast strategy for finding a value for that matches the actual resource consumption against the desired resource consumption .
Because of this convexity, increasing values of will result in a monotonically increasing use of the constrained resource, so an efficient bisection search technique presented by Krongold  can be used to find the value of corresponding to the desired constraint. This bisection search works by starting with low and high values of ; initially, zero and a value of sufficiently high to dominate the term are used, and is calculated for each of these values. For each iteration, a new value of is set at the midpoint of these two values, and its corresponding is computed. If the resource utilization realized from the new is greater than the goal constraint , the range is reduced to the new and the previous low value; if it is less, the new range is the new to the previous high value. This procedure is repeated until the resource utilizations of the new and the previous low are equal.
This bisection algorithm converges quickly; in its use to solve the DMT power allocation problem in , the optimal solution was found within 14 iterations with very conservative initial low and high values. Furthermore, nearly optimal solutions are found even if the search is terminated early; in , of the optimal performance was achieved after only 8 iterations of the bisection search.
4. Lagrangian Formulation of the Optimization Problem
We can apply the Lagrangian technique to the resource-allocation problem in (2) by realizing that we have a utility function analogous to the shown in (3), and several resource constraint functions analogous to . We can therefore transform this problem into a Lagrange form, and by finding suitable values for the Lagrange multipliers remove the constraints on the optimization, yielding an unconstrained problem.
Although the Lagrangian form can be used to transform multiple constraints into Lagrange multipliers, fast bisection searches for are optimal only if a single Lagrange multiplier is used. (Bisection searches for are not known to be efficient or optimal if multiple Lagrange multipliers are used .) For this reason, we convert only the utility-energy tradeoff into a Lagrangian form and leave the CPU and network constraints in place. This results in a problem in the form of the single-resource multicell constrained optimization problem of (5).
Because this transformation matches the Lagrangian form, the theoretical results shown in the previous section can be applied. Specifically, this means that we can optimize the system for a particular average power by finding a value of that chooses configurations that meet this power constraint. If a particular value of results in the optimization choosing a set of application configurations that is equal to the desired power , that set of application configurations maximizes the utility for that power level. Furthermore, there exists a value of that will match the average power consumed by every configuration on the convex hull of the utility/energy curve formed by the set of all possible applications and configurations weighted by the probability of the corresponding workloads.
Critically, in so doing we have eliminated the dependence of the optimization problem across workloads, and we can optimally allocate energy across workloads without considering the cross product of application configurations across all workloads.
Computing the value for for a given value of is equivalent to solving the problem of allocating resources to the applications running in a particular workload; other workloads are considered only in the effect that they have in the search for . In other words, after transforming the original optimization problem into the Lagrangian form, we can find an optimal set of configurations for a particular workload in the larger stochastic allocation problem without doing a search across configurations in other workloads.
To find the value of that maximizes the expected utility (to within a convex-hull approximation) while ensuring that the expected running time of the system is at least some fixed value, we simply do a search over to find the value that minimizes . Because is expressed in terms of , this search does not require evaluating cross products of different workloads; each workload is only optimized once per value of checked.
5. Properties of the Lagrangian Approach to Optimization
This section describes various properties of the Lagrangian optimation technique and uses these properties to analyze the behavior and performance of the Lagranian solution to the stochastic allocation algorithm.
In Section 3.3, we showed that if a particular set of parameters minimizes for a particular value of , the use of the resource has been optimally allocated across the parameter set. Because our power allocation algorithm maps the average power parameter to the resource in the Lagrangian formulation, an argument analogous to the theorem presented there can be used to show that the configurations that minimize the Lagrangian for a particular and the configurations that maximize the utility for the average power that corresponds to that are the same. Furthermore, this is true even if the original utility-energy curve is not convex.
5.2. Computational Complexity
Even in the Lagrangian problem formulation, to compute (and determine the optimal configurations for each application), we need to do an exhaustive search over the configurations of applications running at any given time, to ensure that the best possible use is made of the CPU and Network resources. In addition, the search for the value of that maximizes utility while operating within the energy constraint adds complexity to the optimization problem, and as a result the Lagrange implementation requires more computation than the straight knapsack solver for a single application workload.
However, the amount of extra work required is limited. By nature is a convex function of , so the search for can be done using a fast bisection search that will converge within a small number of iterations [6, 7]. Our present implementation searches up to 18 points and finds to precision of times the efficiency of the most efficient application configuration.
However, for the case where multiple workloads are considered, the search for removes the need to jointly consider the application configurations across different workloads. This results in a reduction in the optimization complexity that is exponential in the number of workloads. For example, consider the case where there are two possible workloads, each consisting of two applications with 16 configurations each (like our Sensor workload). To optimize this system using the traditional approach, we must evaluate the 256 possible configurations of each of the two workloads pairwise, resulting in a total of 65536 combinations of configurations evaluated. Using the Lagrangian approach, however, we need to evaluate the constraints for each workload singly at up to 18 values of , resulting in only 9216 configurations evaluated. This is with only two workloads used; as the number of workloads increases, the benefit of evaluating workloads singly instead of jointly becomes larger and the computational workload of the joint optimization rapidly becomes infeasible.
Another key insight is the nature of the intermediate parameter . Although the Lagrangian is a "synthesized" intermediate parameter, in many cases it has a real-world meaning. For example, in Frank Kelly's work on network pricing for elastic traffic , the Lagrangian values represent the marginal or "shadow" price of a unit of traffic on the corresponding network link. And in , the selected value for represents the tradeoff between the energy consumed by an equalizer filter tap and the amount of interference the filter tap can remove from the signal being received.
In the allocation problem we address here, the intermediate parameter defines a tradeoff between the two optimization targets it connects—in this case, between utility and energy. A high means that energy is at a premium, and that we should only use a configuration if it offers a particularly high utility in exchange for its energy consumption. A low , on the other hand, means that power can be spent relatively freely in exchange for modest amounts of utility. In fact, due to the construction of , is actually the minimum allowable slope between the selected point and the previous point on the utility-energy convex hull. ( This property is the key observation used to prove that there is a value of corresponding to all convex-hull points in the scatter in . ) This can be easily shown using an argument analagous to one presented by Ramchandran et al. in .
where slope is the slope of the utility-energy convex hull at the optimal operating point.
Even with the constraints, we can achieve any particular tradeoff between utility and power by only considering system configurations that have marginal efficiencies–-the change in utility over the change compared to the next lower-utility lower-energy point on the convex hull—greater than or equal to a fixed number . (This observation also provides us with an indication of how we find the range over which we must search for : it is sufficient to search from zero, which will permit any application configuration to run, to a number greater than the efficiency (utility over energy) of the efficient available application configuration in the system.) Furthermore, once we fix a value for , we can continue to use it even if the applications running on the system change! The system will continue to run optimally with the same tradeoff between utility and energy, which means that if similar applications replace the currently running applications they will achieve a similar total runtime and utility. Moreover, if we replace the applications with new ones that offer more utility for energy spent, energy consumption will increase to take advantage of the better opportunities to gain utility for the user; likewise, if new applications are less efficient, energy use will be reduced to conserve energy for the future. The marginal efficiency metric therefore provides a mechanism which permits the actual power consumption of the system to vary in response to the changing workloads in an optimal fashion.
Because our constant as the workload changes is the efficiency metric rather than power, energy, or utility, the system's power consumption can increase at one time to take advantage of the availability of high-efficiency tasks, and decrease at others if no high-efficiency tasks are available.
5.4. Optimality Properties
It is important that although our restated optimization problem remains an NP-hard knapsack problem, it shares important optimality properties with the Lagrangian approach.
First, a fixed applies to all workloads and will correctly allocate energy to different applications, even as the workload changes. As long as the marginal utility remains constant, the allocation of energy to the various applications running at different times will achieve the optimal utility for the energy spent. In fact, if we use any fixed Lagrange multiplier when we allocate utility and energy to the applications running on the system, the resulting system configurations will be optimal in that they will achieve the maximum possible utility for the amount of energy consumed.
Second, as proven in the theorem of Section 3, for any value of the returned solution is optimal in that no other solution has both a larger utility, and a smaller total energy consumption. Therefore the system using Lagrange optimization will always operate at an efficient operating point. And since an appropriate value of can be found to match any point on the convex hull utility-energy scatter plot, as long as the composite utility/energy curve is dense and nearly convex (which will be true for systems with a sufficiently large number of configurations), a value of that consumes energy close to can be found.
Although we are limited to points on the convex hull of the utility/energy scatter, in fact these points are "better" than points off the convex hull in the following sense: if we consider total (integrated over time) utility and we permit the system to achieve additional utility by slightly extending our runtime from the original goal, choosing convex hull points on the utility-energy curve will increase the total utility compared to a solution off the convex hull that comes closer to the desired lifetime. This directly follows from the optimality of the Lagrange (convex hull) solution for any runtime it finds.
Total utility (integrated over time) from a point on the utility-energy convex hull is higher than the net utility from a point off the convex hull that provides the same or greater utility.
If we consider a point on the convex hull, and another point that provides more utility and is not on the convex hull, the efficiency (utility per unit energy) of the point on the convex hull will be greater than the efficiency of the point not on the convex hull. (Otherwise, the point not on the convex hull would also be on the convex hull, a contradiction.)
Therefore, as long as we can use any remaining energy to increase run time and achieve additional integrated utility, we will achieve more utility from the additional time than we would have by using the additional energy earlier. And as we accumulate more different workloads, the convex hull becomes denser and the extra utility we can achieve by using operating points not on the utility-energy convex hull diminishes.
5.5. Implementation Details
The complexity of the internal optimization operation is equal to the cross product of all the configurations of all applications running in a particular workload and each available CPU frequency. This represents a great reduction in computational complexity, because applications that are not active in a particular workload do not need to be considered.
Several effective but suboptimal simplifications can also be made. One is that the CPU frequency of all applications running at a particular time can be set to the same value. By doing so, we reduce the search space to only the cross product of the application configurations, times the number of CPU frequencies, with an increase in power consumption that is bounded by Jensen's inequality to the difference between two adjacent frequency steps.
Also, conventional fast search techniques for solving the multidimensional, multichoice knapsack problem can be applied to estimate with reasonable results. This is especially valuable when the number of applications is high, as the complexity of a full search is higher and the suboptimality of doing a partial search is reduced.
Because is a convex function of , the search for can be done using a fast bisection search that will converge within a small number of iterations ; our present implementation searches up to 18 points and finds to a precision of times the efficiency of the most efficient application configuration.
The probability distribution of the workload is only used to select to achieve the average system power and hence the runtime. Once the value of is selected the probability distribution is not used again; more specifically, the probability distribution is not necessary to determine the configuration of the applications that is used at any particular time. This limits the effect of inaccuracies in workload probability estimates. Although an inaccurate estimate of the workloads' probability distribution will result in a runtime longer or shorter than desired, the system will still run efficiently.
Because conveys all the information about how to select configurations to properly tradeoff between system lifetime and quality of service, it is also possible to design the system to allow the user to control more directly. For example, the user can be presented with a slider selecting between optimizing for quality and system life. If this is done, the probability distribution can be used to provide an estimate of the resulting runtime for the value of selected by the user.
The optimal value of depends only on the probability distribution of workloads that may run on the system; it does not depend on what applications are running at any particular time. Therefore, once an optimal is chosen, it can be used for a long time—until the desired runtime changes or the battery is replaced or charged, or until the probability distribution that was used to compute is no longer valid. Even as the workload changes, the value of we use to compute the optimal allocation of resources for any given workload stays the same, as it represents the optimal division of energy between the current workload and the future.
The same Lagrangian approach used to solve the stochastic allocation problem can also be used to solve the related known-workload problem. For a single workload, simply setting and for the workload maps it into the stochastic framework and all the above proofs apply. The reservations proposed in  can also be accomodated by setting the probability associated with each workload to be the running time of that workload over the total running time of the system.
6. Optimality of the Energy-Greedy Heuristic
One important omission in the prior work by Yuan  is that it does not discuss the optimality of its allocation heuristics. The Lagrangian framework we use to solve the stochastic allocation problem can also be used to make statements about these types of heuristics. Because we compare the performance of the Lagrangian optimizer against the energy-greedy heuristic in Section 7, we digress briefly here to describe the conditions under which the "energy-greedy" heuristic described by Yuan is optimal.
The simplification made by the energy-greedy heuristic is that applications running when the allocation decision is made will continue to run until the system is shut down. Since this assumption describes a subproblem of the stochastic or varying-workload allocation problems, the energy-greedy heuristic is optimal if the applications in fact do not change, and is a good heuristic if the character of the applications running on the system stays roughly the same. However, if the utility or energy demands of the applications change dramatically over time, it may result in significantly suboptimal allocations.
Going back to our wireless camera example, this subproblem would assume that the data is equally "interesting" (and hence has an unvarying utility) for the entire running time of the system.
This unvarying-workload subproblem (and by extension the energy-greedy heuristic) is essentially a constant-power approach to the larger resource allocation problem; at all times it limits power consumption to a value that allows the required lifetime to be achieved given the current energy supply. (The power constraint can vary in response to current energy availability as the optimization is repeated.)
Given a dense set of application configurations, the energy-greedy heuristic results in a near-constant system power (The system power will vary by no more than the difference between the operating point and the next higher-power point on the utility/power curve. If operating points are closely spaced over the powers being optimized across, the utility/energy curve points will be close together and this difference is small.).
Utility is a monotonically increasing function of power, (although this is not true in general, any nonmonotonic points are always suboptimal and will therefore be ignored by the optimization process) and we will always choose to use as much power as possible to achieve the greatest possible utility. As a result, the energy consumption of the system will be as close as possible to given the available application configurations. If the set of application configurations is dense, the actual power will be close to , and when the maximum allowable power is calculated again the result will be near (but perhaps slightly higher than) .
To evaluate the effectiveness of this Lagrangian resource allocation, we use a simulation of the GRACE framework [10, 11]. This simulation is described in more detail in . It provides, earliest deadline first (EDF) scheduling of both the network and CPU, management of applications entering and leaving the system, and power modelling and estimation for both the network and CPU. The system runs a multimedia video encoder that is capable of operating at several utility levels (with varying image sizes, quantizer step sizes, and frame rates) and also permits compression efficiency to vary. The variable compression efficiency allows the system to save energy by reducing CPU demand at the expense of an increase in network-bandwidth utilization .
7.1. Simulation Environment
The network is modeled as having a bandwidth of 500 Kbyte/s and an active power of 0.5 W, corresponding to a per-byte energy cost of 1 J, a data rate and energy per byte similar to common 802.11 b wireless network interfaces. The network is assumed to be reliable as long as the bandwidth constraint is not exceeded, and no protocol or protocol overhead is assumed. Power estimates are generated by multiplying the active power of the network by the estimated or actual network utilization. The CPU energy and utilization estimates are based on the AMD Athlon XP-M 1700+ microprocessor, a model that incorporates voltage and frequency scaling; power is estimated by multiplying the CPU utilization by the power consumed when operating at the selected CPU frequency, ranging from 25 W at 1466 MHz to 6.4 W at 533 MHz.
The desired runtime is set to 600 seconds, and the starting energy of the battery is varied to simulate environments under tighter and looser power constraints. The simulation runs for 600 seconds or until the initial energy supply is exhausted, whichever comes first. Parasitic power demands (such as the display) are not considered; it is assumed that the provided initial energy excludes any parasitic power that would be consumed during the requested running time.
The simulation environment does not presently charge the Lagrange optimization for the processing time and energy spent doing its one-time search for the Lagrange multiplier. The run time for the current implementation of the Lagrange multiplier search is approximately 2 seconds at full processor speed for the "laptop" workload, so it would increase the total energy consumption for the Lagrangian case by about 50 J. We do not charge this energy because in practice it would be amortized over a much longer runtime than the 600 seconds used in these simulations.
For these simulations, we use the GRACE framework and adaptive encoder application described in , extended to add the ability to send uncoded as well as encoded macroblocks . The application is run on input streams with two different image sizes, CIF ( ) and QCIF ( ). For each image size, the resource requirements can be reduced at the cost of decreasing utility by decreasing the frame rate from the base of 10 (CIF) or 15 (QCIF) fps. The system also supports reducing the quality by increasing the quantizer step size for CIF encoding, although these configurations are relatively inefficient (in terms of utility per unit power consumed) and are therefore not selected by the optimizer.
Each operating mode allows application adaptation: 15 available compression modes when the quantizer step size Q is 6, and 6 modes when Q is 12.
Because choosing meaningful values for the base utility would require extensive human trials, values were instead assigned by hand. These particular values for utility were selected to ensure that the utility is a monotonic function of resource utilization and hence energy. They do not result in a convex energy/utility curve; this is intentional and intended to put the Lagrangian approach at a slight disadvantage.
In addition to the base utility, which is associated with the application itself, each time an application starts it is assigned a "weight" by the user. The weight connects the base utility of the application with the user's perception of its importance—it is a "utility mapping function." The implementation multiplies the weight assigned by the workload by the base utility rate of the application to find the actual utility rate for each potential application configuration. The higher the weight, the higher the resulting utility, and the more likely it will be that the application will be allocated enough energy, CPU time, and network bandwidth to operate at a high quality level.
7.2. Simulation Workloads
We implement these simulations by defining two different prototype workloads, consisting of the CIF and QCIF versions of our adaptive encoder application. The first "laptop" workload is intended to represent a reasonable variation in desired applications and utility; the second "sensor" workload is a favorable workload intended to highlight the improvements in total utility that can come from allocating energy only to the most beneficial applications.
The prototype workloads list the possible application sets, the weight for each application, and the probability that this application set is active. We then generate the actual workload by choosing a workload from the prototype according to the associated probability distribution for each 30-second slice of a 600-second simulation run. The input stream is a composite of several MPEG test sequences, treated as a circular array. As part of the workload creation process a starting position for each application invocation is chosen randomly (with a uniform distribution) from the frames in this composite stream.
In all cases, the original probability distribution from which the actual workloads are drawn is used along with composite statistics about the application's resource demands to compute the value for used for the Lagrangian optimization.
It is important to note that the global allocator is permitted to refuse any offered jobs, and that each application can run at any one of several different quality/utility levels. This means that the actual energy consumption of an offered workload can vary down to zero, if none of the offered applications receives an energy allocation.
7.2.1. "Laptop" Workload
One possible explanation for this type of workload is a laptop participating in a video teleconference. As the video conference progresses, various portions of the video (for instance, slides, the user, canned video, and animation) of varying importance start and end. This results in the entry and exit of different encoders with different frame sizes and importance.
7.2.2. "Sensor" Workload
This type of workload distribution could be found in a sensor network. The rare high-value operations occur when the sensor has detected something of interest and the operator is likely to be actively viewing the sensor's output; the common low-value operations occur when the system has not detected anything of interest and therefore is unlikely to be needed or monitored.
7.3. Simulation Results
The leftmost column of graphs shows the total realized utility—in other words, the sum of the utility values multiplied by the running time and the weight of each application—over the 600 seconds the system is allowed to run. The middle column shows the amount of time that the system runs before it shuts down, either due to running out of time or exhausting its energy. The rightmost column shows the total energy consumption of the system. None of these totals include the time and energy that would be spent finding the optimal value of as it is assumed to have been computed offline. The overhead of allocating resources to each application entering and leaving the system is, however, included.
Each graph has a solid darker line representing the results for the Lagrangian optimization and a dashed lighter line representing the "energy-greedy" heuristic. The horizontal axis on all the graphs is the starting energy of the battery, expressed in terms of the average power permitted over the 600-second desired runtime; the starting energy in Joules is the value in Watts shown 600 times. The vertical axis on the "utility" graphs is utility units based on the application utility and weightings; on the "time" graphs it is seconds, and on the "energy" graphs it is once again in terms of power averaged over the desired runtime of 600 seconds. The performance is sampled across average power limits at every two Watts from 5 to 25 W.
The minimum and maximum values across all 10 workload sequences are shown as error bars on the "average" graphs. The darker error bars correspond to the Lagrangian optimizer, the lighter error bars correspond to the energy-greedy heuristic. Note that the minimum and maximum values for each starting energy are each selected independently and do not represent any single workload.
7.3.1. "Laptop" Workload
The results for the "laptop" workload are shown in Figure 1. We see that, on average, there is a significant increase in utility when the starting power is low, and no significant change in utility or energy consumption when the starting power is high. At an average power constraint of 5 W, we improve the average achieved utility by over by pushing the power consumption from times that only low-utility tasks are running to other times when higher-utility tasks are available. However, once the starting energy is sufficient to allow the average power drain to exceed 15 W, there is no benefit from the use of the Lagrangian approach.
It can also be seen that in some cases, the total utility is reduced modestly. The worst loss of utility observed is and occurs due to under-using energy; this case is shown as "Workload 3." Here, at an average power limit of 9 W, only of the original energy is used at the end of the end of the 600-second desired runtime. This occurs when the actual application selections have a lower utility than the prototype distribution. Although the applications to be run are selected from the probability distribution provided to the code that computes the optimal Lagrange multiplier, the workload list is short enough that significant variations from the mean distribution can occur.
7.3.2. "Sensor" Workload
The results for the "sensor" workload are shown in Figure 2. Because this workload was constructed to show a large benefit from the Lagrangian optimization, we see an average improvement in utility of over when the average power is limited to 5 W. (It is important to remember that a suitably constructed sequence could realize an arbitrarily large utility improvement.) As the average power increases, the benefit from the Lagrangian approach falls; at 13 W, the average improvement in utility is . Above 17 W, there is no improvement because the Lagrangian optimizer and the energy-greedy heuristic yield exactly the same configuration.
In fact, for several of the workloads, the utility curve is close to flat; for example, this is true of the second and fourth workloads shown in Figure 2. The flat utility curve is because, in these cases, there is enough energy to run all the high-utility tasks at full quality (achieving the highest utility), and the low-utility tasks do not contribute significantly to the total utility.
In our test system, the optimization process is driven entirely by estimates of the system loading and utility for the various available when the system first starts up. Since these estimates are by nature stochastic (they represent a "typical" operating condition rather than the specific operating condition that is actually encountered), inaccuracies in these estimates of energy and utility that go into choosing an appropriate can result in the system behaving suboptimally.
At the 15 W average power level, we see the effects of inaccuracy in the initial estimates resulting in a system outage quite clearly. It manifests as a dip in running time across all the workloads, which in some workloads results in a noticeable reduction in utility. This dip occurs because when we do the search for , the system estimates that if all applications are run at their highest possible utility (i.e., is set to zero), the average power will be slightly less than 15 W. In reality, though, the power demand is slightly higher. Because the system uses more energy than is predicted as it actually runs, allowing the applications to all run at maximum utility does not conserve enough energy to run until the end of the run time. Therefore, if a high-utility task appears at the end of the sequence of workloads, it will not run and the system will be unable to achieve the maximum possible utility.
This "dip" is partly, but not entirely, due to a systemic bias in the predictions: the power predictions made when is calculated do not include energy used to allocate resources to applications as they enter and leave the system. There is also some systemic undercounting in the predicted cycle count, because the procedures used to create these tables do not accurately account for the per-application adaptation overhead. Although these systemic biases could have been corrected in the initial predictions, and various other techniques (such as implementing an energy reserve and recalculating periodically based on actual system performance) could have been used to correct for stochastic variation, we felt that the resulting reduction in running time and loss of system utility was illustrative of the possibility of outage that results when stochastic techniques are used for optimization.
It is also possible for an outage to result because the workload has an atypically high occurrence of high-utility workloads—that is, more high-utility (and therefore energy-consuming) workloads appear than the probability distribution indicates. In these cases, the system may terminate early due to energy exhaustion. Because the 600 seconds lifetime we use in these experiments is relatively short, this effect can be easily seen in Workload 1, where the total utility is much higher than the other workloads, but the system shuts down early if the battery holds only enough energy for an average power of 5 W and fails to run a high-utility task. The probability of an outage this large would be diminished in practical implementations of systems using our stochastic allocation algorithm by longer total runtimes and more variation of the workload. Implementations could also recompute periodically, which would also combat outages by forcing some of the high-utility tasks to run at a lower power level, helping to allow the system to achieve its required runtime. It is worth noting, however, that doing so would in general reduce the total utility achieved.
Lagrangian optimization techniques can be productively applied to the problem of optimizing the allocation of a fixed pool of energy across multiple applications as they enter and leave a system. The Lagrangian approach to resource allocation requires only that the probabilities of various workloads to be known; foreknowledge of the actual schedule is not required. Compared to existing constant-power optimization algorithms, the Lagrangian procedure can provide significant improvements in achievable utility when energy is at a premium. Depending on workloads and energy availability, the Lagrangian allocation approach can increase total utility by a factor of two or more.
We have also shown problems that an actual implementation of this approach would encounter. The approach is sensitive to the accuracy of the probability distribution of the expected workload; the results show that mismatches result in consuming too much energy and terminating early, or consuming too little and achieving less than the best possible utility. By periodically recomputing the value of taking into account changes in energy availability and probablility distribution, inaccuracies in the predictions can be corrected.
This material is based upon work supported in part by the National Science Foundation under Grant no. CCR-0205638.
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