Duplication elimination in cache-uplink transmission over B5G small cell network

In this era of the digital world, data play a central role and are continuously challenging spectrum efficiency. With the introduction of enriched multimedia user-generated content, the challenges are even more aggravated. In this vein, uplink caching is considered as one of the promising solutions to effectively cater the user’s demands. One of the main challenges for uplink caching is duplication elimination. In this paper, a cache enabled uplink transmission with a duplication elimination scheme is proposed. The proposed scheme matches the mobile’s data to be uploaded with the cached contents both at mobile station (MS) and small base station (SBS). In contrast to existing techniques, the proposed scheme broadcasts the cached contents at an SBS to all the MSs under its footprint. This provides MS an opportunity to exploit the list of cached contents before uploading its data. A MS only uploads its data if it is not already cached at an SBS. This significantly reduces duplication before the real transmission takes place. Furthermore, the proposed technique reduces energy consumption in addition to improving spectral efficiency and network throughput. Besides, a higher caching hit ratio and lower caching miss ratio are also observed as compared to other schemes. The simulation results reveal that the proposed scheme saves 97% energy for SBS, whereas 96–100% energy is saved for MS on average.

keys. As a result of duplication elimination, MSs only upload the dissimilar contents. Furthermore, the cache replacement policies are also used to evict contents in case of cache overflow. The proposed scheme also improves the spectral and energy efficiency of uplink transmission by improving data rate in addition to reduced latency and a cost of communication. The main contributions of this paper are as following: • An uplink caching model to improve the energy efficiency.
• Duplication elimination at an MS that reduces the upload time and an MSs' online time with an SBS. • Improving network capacity of a small cell network by reducing the amount of unnecessary uploads thus improving spectral efficiency. • Comparing our proposed model with other schemes to show its effectiveness.
The remaining paper is organized as In Sect. 2, the Related Work is discussed, Sect. 3, describes a System Model including Symbol Notation, Energy Consumption model, SBS cache model. Section 4 describes Broadcast Cache Assist Uplink (BCAU) of the proposed scheme including SBS Generated SBS Broadcast Items List (SBSBIL), Duplication Elimination, Matching overview, Cache Replacement Policies (CRP), and other concepts. In Sect. 5, Simulation Experimental is including Experiment Setup, Performance Evaluation Metrics, and results. In Sect. 6, the conclusion is presented.

Related work
The existing works are based on dealing with caching, offloading, uploading contents. The different areas of the research include wireless local area network (WLAN), small cell base station (SCBS), content-centric network (CCN), Offloading, . . . etc. also different methods about the matching of contents.
Various techniques are devised to address caching challenges, such as [20][21][22]. In [20], the authors presented a bidirectional cache for WLAN peer-to-peer architecture to share the bandwidth between uplink and downlink. In [21], the notion of the "Mobile 3C" system is presented as communication, computing and caching as primary functionalities for the sustainable growth of mobile systems. In [22], the authors proposed an in-network caching scheme to speed up data delivery, mitigate server load, and reduce network traffic in Content-Centric Network (CCN).
Many researchers have addressed the problem of uploading contents while considering the cache [23][24][25][26][27][28][29][30]. In [23], the authors proposed a mobile video uplink caching algorithm to increase video quality via recover lost video frame in real-time video transmission. The authors in [24], proposed a multiuplink caching proxy called "squid" as a virtual machine and a bridge to communicate with the physical devices to improve the web browsing speed. In [25], the researchers suggested an upload cache at edge-network to assist in uploading UGC and providing services. The upload cache reduced user online time for uploading the content and reduced the peak traffic volume between edge networks and data centers. In [26], the authors designed an upload acceleration service framework for mobile devices on virtual network architecture host-based WiFi as the access point. That architecture is designed in threefolds: upload acceleration service to the designated flow of data, a two-tier architecture where UGC is first cached to the front-end, then to the back-end, finally to the internet. That architecture reduced upload time and achieved a stable transmission throughput for MSs. In [27], the authors studied the performance of 3G mobile data offloading at WiFi networks. The data are offloading saved battery power without any delay in transmission when traffic is increased. In [28], proposed a smart offloading proxy (SOP) as a storing system to WiFi Base Station for temporarily caching content uploading overcrowded events. The SOP improved users'experience and reduced scheduling time of uploading user-generated content and then delivered that multimedia to the target social network. In [29], the authors have designed a data acquisition system (DAQ) for recording high speed continuous random bits in real-time. The DAQ served to achieve three goals: Acquisition as the interface to the high-speed random data, cache to stored data, and then uploaded data to personal computer (PC) via Gigabit Ethernet as data uplink. In [30], proposed "In-edge Artificial Intelligent (AI)" using Deep Reinforcement learning techniques and federated learning framework for optimizing mobile computing, caching, and communication to make edge system intelligent enough to reduce the unnecessary load of the communication system and then make cognitive and adaptive mobile communication system. However, any previous works [23][24][25][26][27][28][29][30] did not address cache-uplink transmission and duplication elimination.
Most research has focused on the downlink cache scenario, and many techniques have been proposed in this regard to coping up with the tsunami of mobile data traffic. They are surveyed in [31]. Also, the uplink-cache has a far more significant impact. Few kinds of research have been done on cache-enabled uplink transmission in cellular networks. In [32], the authors presented a theoretical framework for (1) cache-enabled uplink transmission in wireless small cell networks, (2) duplicate elimination of similar contents at SBS via matching the hash key of chunks of the file after uploading real content, (3) cache management by using FIFO, random and probabilistic content scheduling strategies. In [33], the authors presented a cache-uplink of HetNet presented with stochastically distributed MIMO Base Stations (BSs) for temporary caching of UGC.
Besides, many researchers have proposed different algorithms to perform comparison among the same kinds of contents such as video, audio, image, text, . . . some of them as is shown in Table 1.

System model
In this section, the system model of the cached-enabled uplink placed at an SBS is presented. The Mobile Stations have contents to uploading for sharing with others or store it somewhere on the internet that contents may be available at the SBS'cache or not. The system model shows the structure of the infrastructure of the cell network, the energy consumption by MSs and SBS, and the data availability in the SBS'cache presents. This section includes system notation and three models as the network, the energy consumption, and SBS'cache. The system model is shown in Fig. 1, and introduces in detail as are the following.

Symbol notation
The following table shows the notations which are used in this work, as shown in Table 2.

Network model
The cellular network in B5G consists of small cell network (SCN) and MBS with an Innetworking cache-enabled uplink place at SBS. The SCN consists of two elements (1) An SBS (2) M Mobile Stations (MSs). The locations of all elements are assumed to be fixed. An SBS is equipped with cache and is connected with the providers of services/ data either, the data center or the cloud via a wireless backhaul link or wire as fiber optic, while An MBS is connected with the core network or clouds through a wired or wireless connection. An orthogonal frequency division multiple access (OFDMA) is considered to be scheduled on both the MBS and SBS orthogonal resources. The SBS is served a set of M = {MS 1 , MS 2 , ...., MS M } mobile stations. The MSs are assumed to be distributed as an independent Poisson Point Process (PPP) φ MS with a sufficiency of high density MS . All MSs are connected to an SBS via wireless links. The uploading of Mobile'content is transmitted through wireless transmission, wireless backhaul, and then wired to the internet. The content is produced by mobile or obtained from other sources such as Bluetooth or WLAN direct, social media, . . . . for example, videos, images may be shared by many users in close geo proximity. In this regard, cache-enabled uplink can effectively reduce unnecessary uploads, making the transmission energy and spectral efficient. All MSs can produce a hash key of the entire content. The other attributes of contents, such as the name, size are also computed. The hash key of content is essential for assigning an index of content in the SBS cache [55][56][57]. When MS intends to upload content, the first step is to generate a hash key and another set of content attributes. After this step, the MS looks for the possibility Many-to-many matching game [51] 2014 The dissimilarity between video frames [52] 2013 Image-text A neighbor-aware network to image-text matching [53] 2019 Bi-directional Spatial-Semantic Attention Networks (BSSAN) [54] 2018 of transmitting that content to an SBS via matching the attributes of new content and the cached contents at an MS before the real transmission takes place. If any content gets matched with the new content, then MS only sends the message of target destination (MoTD) to forward copy from the SBS cache to the target destination. Otherwise, the new content is uploaded. The message of target destination (MoTD) contains the hash key with other attributes of new content and the target destination

Energy consumption (EC) model
In this subsection, the energy consumption of the uplink transmission only presents. Energy Consumption is defined as the amount of energy spent by MS or SBS to execute all operations related to data communication and caching. Assume the mobile has two probabilities for uploading content: the content can be hit or miss in the cache. In the case of a cache hit, that means the content is available in the SBS cache. The MS does not need to upload that content. Just only send the MoTD to forward a copy from the SBS'cache to the target destination. For that, the energy consumption is computed for matching, MoTD, and forwarding copy from the SBS'cache. While in the case of a cache miss, that means the content is unavailable in the SBS'cache. The MS needs to upload the real content. Thus, the energy consumption will be computed for uploading the real content and execute all other operations related to content. That means the transmission power of MS or SBS is consumed for uploading the real contents or the MoTD and execute all operations related to data communication and caching such as matching, reception. To investigate that, the energy consumption is computed at two sites, SBS and MSs according to [58].

Energy consumption of SBS ( SBS EC )
The SBS EC is the total amount of energy spent by SBS to execute all operations related to data communication and caching from reception the request of the uploading of MS until the end of the session and then forward that content to MBS/cloud and is given as The individual components can be defined further as

Energy consumption of mobile station ( MS EC )
The MS EC is the total amount of energy spent by MSs to execute all operations related to data communication and caching from mobile sends the request of uploading content until the reception of SBS's reply and the end of the session and is given as where E rc take a value of the cost of received packet of SBS'reply (as AKC message) The individual components can be defined further as The average energy consumption by MSs is given as

SBS cache model
In this subsection, the evaluation of the data availability of the SBS'cache is present. The size of SBS cache cache size is the maximum number of N contents that SBS cached. The set of N contents is denoted as Each content has its size and is indicated by S C item . Also, Each content has its popularity, which is computed by a Content Controller (CC) of SBS, etc. [9] based on different events such as recently and frequently used, deadline, times of shares, views, downloads, and uploads, . . . . The popularity of content is modeled by using Zipf distribution according to [60].
where δ determines the peakiness of the distribution and takes a value between 0 and 1.0 and L Popu is the least popular of content. (2) An SBS stores the most popular content. When MS intends to upload the content, there is a chance that it might not be present in the SBS cache. Two measurements presented in [59] are used for verification of the availability of content.

Cache hit
A cache hit ratio occurs when the content is actually available in the cache. It can be defined as Hit n takes the value of 1 if the content is available in the cache, while Miss n values opposite to that.

Cache miss
A cache miss ratio occurs when the content is unavailable in the cache, and Uplink transmission will occur. It can be defined as

Broadcast cache assist uplink (BCAU)
The BCAU is presented for performing the matching the attributes of new content and cache contents at Mobile to discard the similar content and uploading the dissimilar content before starting the transmission of the real content. The attributes of new content are originating from the MS, while the attributes of cache contents are available in the SBS'cache. The SBS produced the list of the cache contents, and their attributes and that list is broadcast to all MSs to buffered and then use when needed only for matching. Due to the limited storage capacity and the availability of contents at the SBS'cache and then the uplink's transmission power limitation. The mobile station would not upload the same cache contents. Also, an SBS cannot cache the same contents again. Based on this problem, this work will present a new matching scheme at the MS level, which consists of four main after pillars (1) The SBS Broadcast Items List, which is referred to as SBSBIL. (2) Fig. 2, and the details are as follows:

SBS generated SBS broadcast items list (SBSBIL)
The SBS Broadcast Items list (SBSBIL) lists the SBS'cache contents and their produced attributes. An SBS is produced the SBSBIL and broadcast to all MSs. The primary goals of SBSBIL are as follows: 1 To perform matching and duplicate elimination at MSs level rather than at the SBS level.  According to Eq. (7), the contents of SBSBIL are selected based on the content's popularity, calculated using Zipf distribution. The probability of selected content to be in SBSBIL can be obtained as

Classification of cache contents
Different types of contents are stored in SBSBIL. Each content has its attributes based on its type and quality. To achieve a practical approach for matching and determining the duplicated contents and then reduce the time of waiting and matching. The contents classification approach is utilized first based on the type of content such as video, audio, text, image, etc. Each mobile intends to upload content. That content may be available in SBSBIL. Certain features and attributes of the contents are required to be able to discard duplication. As mentioned before, the SBS cache/SBS-BIL stores N contents. Each cache/SBSBIL content is mentioned by C item , whereas N item mentions a new content. Each content has K attributes. The mobile produced the attributes of new content and stored them in the matrix. Also, the attributes of the cache/SBSBIL contents are stored in another matrix. The two matrices are derived as follows: Matrix-2 is called a matrix of attributes of new content. It is a 1 × K Row vector matrix. It stores new content and the values of its attributes. The row presents a new content, while K columns present the values of attributes of new content. A Matrix-2 can be expressed as Matrix-3 is called the matrix of attributes of cache/SBSBIL contents of the same type of new content. It is an N × K Rectangular Matrix. It stores the cache/SBSBIL contents. The matrix length is equal to the total number of cache/SBSBIL contents with the same new content type. The N rows present cache/SBSBIL contents of the same type of new content, while K columns present the values of attributes of that contents. A Matrix-3 can be expressed as

Duplication elimination
Deduplication is an efficient data reduction technique to create the underlying space needed to store a file. The duplication of a file can either be sub-file or the whole file. In this work, the whole file-level based on its attributes is used because the whole-file duplication is simple and eliminates file-fragmentation concerns and reduces the latency by avoiding redundancy [61]. The duplication of similar content is determined via matching the attributes of new contents and cache/SBSBIL contents.

Matching overview
Matching algorithms often express the difference in covariant values between a treated subject and a potential control [62]. The development algorithm of matching the contents of the same type such as multimedia, text, audio, . . . is via creating a generative model that includes the matrix-2 as a control item and the matrix-3 as a treated item, then matching between the two matrices. Table 1, shows different algorithms of matching the contents of the same type such as audio, video, text, image. These algorithms are not suitable to be used for matching at SBS or MS because of the limited and constrained resources. Also, these algorithms produced the values of matching based on a single attribute such as a hash key. The reason behind complexity is that a single attribute cannot give an effective matching output to compare between the different contents of the same type when each content has its attributes based on the quality of made it. Apart from the above issues, the newly generated content and cache/SBSBIL contents are independent contents having their attributes.

Matching algorithm
We propose a new scheme for matching the common types of content mostly shared between users are havingtheir own or different attributes. The matching between the matrices of attributes of contents and elimination duplication is performed at MSs before uploading the real content as shown in Fig. 2.
Two functions perform the matching and elimination duplication: One is the function of attribute comparison, by making a parallel comparison between the matrices of the attribute of contents, whereas the second is the Method of Content-Defined chunking.

The method of dissimilarity
To investigate the first function of attribute comparison by calculating the similarity measure using Dissimilarity for Attributes of Mixed Types [63]. The dissimilarity between the contents of the two matrices-(2&3) performed as are the following: Matrix-4 is the combination of the two matrices S1 1×K and S2 N ×K into one matrix is call Z, and is given as The last row of matrix Z is the new content and the values of its attributes as shown in matrix-2. However, each content has different kinds of attributes such as nominal, interval,ratio, and ordinal. Formula (12) is used to calculate the value of the dissimilarity of the string attributes. Simultaneously formula (13) is used to calculate the value of the dissimilarity of the numeric attributes.
where (i, j) is the sequence of the same attribute. P item is the attribute of content (got from matrix-4 ( P item ∈ Z (N +1)×K )) and h run overall non-missing objects for attribute P item .
Equations. (12 & 13) were obtained in a matrix format, which is used to compute the overall dissimilarity between new content and the cache/SBSBIL contents suing where the indicator δ Determine the value of threshold of the matching th sh . The decision indicates the new content is similar to any cached/SBSBIL content, if the Sim(x (1,i) , y (i,j) ) is larger than or equal to threshold of matching. Finally, a similar file will be discarded however the dissimilar file will upload.

The method of content-defined chunking algorithm
The partition point is determined based on the size of the content as a parameter of the chunk'size chunk size also use to determines the number of chunks generated. Dividing each content based on the chunk size into many partitions. Compute each chunk'fingerprint with a magic value and then store it into a matrix, as matrices (2&3). A comparison between that two matrices is performed to find a match. This method is not suitable for our model because more time and space are wasted. This method is also performed based on the size of content, while the same content has different sizes based on the quality of making it, especially videos and images.

Cache replacement policies (CRP)
The previous section's discussion makes the matching new content with SBSBIL/cache contents to eliminate duplication, further improving the efficiency of uplink transmission of MSs and SBS. However, another problem is to vacate a slot i.e., what to do when the cache is full?. The proposal scheme utilized a greedy algorithm to compute cache'free space and then identify an empty slot for caching new content to solve this problem. The SBS cache size is cache size to store N contents within various sizes S C item and the size of new content is S N item . The free space of the cache of the SBS Cache fs is given as After the computation of the free space of the cache will mark the possibility of finding the empty slot in the cache to store new content or not. In the case of the free space is equal to or smaller than the size of the new content, the cache algorithm will execute. A cache algorithm is a list of instructions that direct the contents, which should be discarded for different reasons, such as the SBS'cache is full and reduce the cache miss ratio [64]. The caching algorithm is divided into two categories as scheduling strategies and replacement policies. In this paper, an efficient cache replacement policy is utilized to reduce as much unnecessary data as possible to improve transmission efficiency and increase network capacity. The fast and lightweight replacement policies are suitable for SBS'cache as are the following: 1 Least recently used (LRU) is used a counter to keep the times of access to cached content such as times of uploads, downloads, views, shares, . . . for the cache contents. The cache contents of the recent access would keep near the top of the cache, while the least recent content at the bottom of the cache. When the cache is full, the content at the bottom of the cache is discarded first because it is least recently used [65]. 2 Least frequently used (LFU) is used a counter to keep track of how often the cached content is accessed, which is calculated by how many times it was accessed. The least often used content will be discarded from the cache because it has the lowest frequently count [66,67]. 3 Most recently used (MRU) is opposite to LRU [67]. 4 The content length (LEN) Algorithm focused on content size to determine which content will be discarded from the cache based on the size of the content by the least number of references. The content of the factor's small value will be discarded from the cache of SBS [68].
These algorithms have time complexity O(1) and widely used in many applications such as YouTube, Twitter, Facebook [69][70][71][72]. The SBS only decides eviction when dissimilarity and SBS'cache are full.

Proposal BCAU algorithm
This subsection presents the matching and duplication elimination algorithm as a dynamic algorithm for matching the attributes of new contents and the SBSBIL (16) contents at MSs level to discard the similar content and uploading the dissimilar content. The result is programmatically extracted, as shown in broadcast cache assist uplink (BCAU) Algorithm in Algorithm 1.
This algorithm can implement by using eight steps are as the following:

Based on
Step-4, if the matching factor is greater than or equal to the matching threshold, then the similarity. Otherwise dissimilar. 6 If the similarity, then the MS sends only the MoTD and forward copy from the SBS'cache to the target destination and ends the session. Otherwise, check the free space of the SBS'cache using Eq. (16). 7 If an empty slot is available and the cache's free space is less than the size of new content, then an MS will receive the index of an empty slot to start uploading the actual content. Otherwise, an SBS runs one of the CRP to get an empty slot and then sends the empty slot index to the MS to start uploading the actual content. 8 Uploading completed, end session and then SBS would forward the real content to the target destination.

Complexity analysis
The solution to the cache uplink problem without duplication contents is solved by an algorithm 1 called BCAU . A cache is stored N contents. Each content has K attributes.

Simulation experiments
In this section, the experimental setup and metrics of performance evaluation are present to obtain simulation results. Next, analyzed and validated the system model and showed the proposed scheme's performance based on obtained simulation results.

Experimental setup
The simulation is performed as follows: Three main-models as SBS-cache, SBS-nocache, and SBSBIL. Five sub-models for each main-model to test the energy consumption, based on the number of Mobile Stations as sparse and dense deployment. Also, five scenarios to measure the impact of Cache Replacement Policies (CRP) (LRU, MRU, LFU, and LEN) on the Cache hit ratio and Cache miss ratio only for the SBSBIL model. Based on the number of the cached contents, ten scenarios to test the impact of CRP are observed on the SBSBIL model. The simulation parameters are shown in Table 3.

Performance evaluation metrics
For a comprehensive evaluation of the performance of the proposed scheme through compared different models, the evaluation of performance was tested and assessed by different metrics as are following: the Energy Consumption by SBS using Eq. (1) and the average energy Consumption by MSs using Eq. (6), Cache Hit Ratio using the percentage of Eq. ( 8), Cache Miss Rate presented using the percentage of Eq. (9) and Eviction Cache contents.

Experiments results
Simulation is performed considering the main models and their sub-models in different scenarios with the same simulation parameters are listed in Table 3. The final result is validated by the different performance metrics, as shown in different Figs. 3, 4, 5, 6 and 7.  Figure 3 shows the EC-SBS with SBS-BIL is less than with SBS-no-cache and SBS-cache. On the other hand, the EC-SBS with both SBS-no-cache and SBS-cache increased as the MSs increased. This is because the matching and duplication of similar contents are handled locally at the MSs level which is reduced the amount of redundant communication and decrease the power consumption of SBS to reception and forwarding the number of contents which mobiles intended to uploading.  Figure 4 shows the average energy consumption by MSs (AEC-MS) with SBS-cache, SBS-no-cache, and SBSBIL with different number MSs. Figure 4 shows that AEC-MS with SBSBIL is less energy-consuming as compare to SBS-no-cache and SBS-cache. On the other hand, the AEC-MS with both SBS-no-cache and SBS-cache increased as the MSs increased.

Average energy consumption (EC) of MSs
In both SBS and MSs, the energy consumption of SBSBIL is less because the matching and duplication elimination of similar contents are handled locally at the MSs level which reduced the transmission power and the cost of communication of uplink transmission by limiting the amount of uploading contents and reduce the cost of executing the other operations. While the SBS in the SBS-no-cache model is an intermediate that receives and immediately forwarded content to an MBS or a cloud. We conclude that the  Figure 5 depicts the percentage of eviction cache contents by applying CRP as LRU, LFU, MRU, and LEN, respectively, under the different number of cached contents. Figure 5 shows that the LRU performed better in vacating more cache contents, hence achieving more free space with less energy consumption. The eviction is ginning from the bottomup of cache until it reaches the unequal threshold of eviction. LRU also kept different  types of high and low popularity at the SBS'cache for later matching, and thus the cache hit ratio increased. Figure 6 shows the hit ratio of the SBSBIL model considering the CRP (LRU, MRU, LRU, and LEN) with the different number of MSs. Figure 6 shows that the LRU is a higher Hit ratio than MRU, LFU, and LEN because contents with rank top in high recently used and popularity are kept in the cache for matching. Also, the number of requests increased due to the addition of MSs causes the hit ratio to become high. We conclude the cache hit ratio with LUR increased when the number of MS increased. Figure 7 shows the miss ratio of SBS'cache of the SBSBIL model considering the CRP (LRU, MRU, LRU, and LEN) with different number of MSs. Figure 7 shows that the miss ratio with LRU is less to compare with MRU, LFU, and LEN. Also, the cache miss ratio with LRU is decreased with an increase in the number of MSs.

Comparison with previous work
This proposed scheme performed the matching at MSs level before transmitting the real content with considering all the attributes of different content types. The existing work did matching at SBS or BS level after transmitting the real content based on the hash key of the contents only. The hash key cannot precisely detect similar contents because the same content can have more than a hash key based on the method used to produce the hash key as only content, the name with or without extension. The network environment of existing work is B4G/4G, while the proposed scheme is B5G. To objectively evaluate this work's purpose, cache uplink transmission is compared with the methods proposed by [32] which have been discussed in the related work section. The results of the comparison are shown in the following Table 4.

Conclusion
In this paper, an uplink cache scheme to improve the efficiency and increase the capacity of a small cell wireless network is proposed. The proposed scheme is called broadcast cache assist uplink (BCAU) to match the attributes of new content and cache/SBSBIL contents at MS level to detect and eliminate duplication of similar content before the transmission of the real content. During matching, the connection between SBS and MS remains idle till SBS reception the request of MS to upload the real content depending on the decision of matching. The matching decision is either to avoid uploading the new content just sent MoTD only (in the case of similarity) or uploading the new content (in dissimilarity). The overhead and online session time are reduced, while the capacity and efficiency of SBS and MBS are increased. Besides, bandwidth utilization is improved without requiring extra resources. To improve the transmission between MSs and SBS, fast and lightweight Cache Replacement policies as (LRU, MRU, LRU, and LEN) are investigated to provide free space in SBS'cache by evicting some contents in case of a cache-overflow. The simulation results show that the proposed scheme saves 97% energy for SBS, whereas 96-100% energy is saved for MS on average.