Proxcache: A new cache deployment strategy in information-centric network for mitigating path and content redundancy

One of the promising paradigms for resource sharing with maintaining the basic Internet semantics is the Information-Centric Networking (ICN). ICN distinction with the current Internet is its ability to refer contents by names with partly dissociating the host-to-host practice of Internet Protocol addresses. Moreover, content caching in ICN is the major action of achieving content networking to reduce the amount of server access. The current caching practice in ICN using the Leave Copy Everywhere (LCE) progenerate problems of over deposition of contents known as content redundancy, path redundancy, lesser cache-hit rates in heterogeneous networks and lower content diversity. This study proposes a new cache deployment strategy referred to as ProXcache to acquire node relationships using hyperedge concept of hypergraph for cache positioning. The study formulates the relationships through the path and distance approximation to mitigate content and path redundancy. The study adopted the Design Research Methodology approach to achieve the slated research objectives. ProXcache was investigated using simulation on the Abilene, GEANT and the DTelekom network topologies for LCE and ProbCache caching strategies with the Zipf distribution to differ content categorization. The results show the overall content and path redundancy are minimized with lesser caching operation of six depositions per request as compared to nine and nineteen for ProbCache and LCE respectively. ProXcache yields better content diversity ratio of 80% against 20% and 49% for LCE and ProbCache respectively as the cache sizes varied. ProXcache also improves the cache-hit ratio through proxy positions. These thus, have significant influence in the development of the ICN for better management of contents towards subscribing to the Future Internet

Flexpop: A popularity-based caching strategy for multimedia applications in information-centric networking

Information-Centric Networking (ICN) is the dominant architecture for the future Internet. In ICN, the content items are stored temporarily in network nodes such as routers. When the memory of routers becomes full and there is no room for a new arriving content, the stored contents are evicted to cope with the limited cache size of the routers. Therefore, it is crucial to develop an effective caching strategy for keeping popular contents for a longer period of time. This study proposes a new caching strategy, named Flexible Popularity-based Caching (FlexPop) for storing popular contents. The FlexPop comprises two mechanisms, i.e., Content Placement Mechanism (CPM), which is responsible for content caching, and Content Eviction Mechanism (CEM) that deals with content eviction when the router cache is full and there is no space for the new incoming content. Both mechanisms are validated using Fuzzy Set Theory, following the Design Research Methodology (DRM) to manifest that the research is rigorous and repeatable under comparable conditions. The performance of FlexPop is evaluated through simulations and the results are compared with those of the Leave Copy Everywhere (LCE), ProbCache, and Most Popular Content (MPC) strategies. The results show that the FlexPop strategy outperforms LCE, ProbCache, and MPC with respect to cache hit rate, redundancy, content retrieval delay, memory utilization, and stretch ratio, which are regarded as extremely important metrics (in various studies) for the evaluation of ICN caching. The outcomes exhibited in this study are noteworthy in terms of making FlexPop acceptable to users as they can verify the performance of ICN before selecting the right caching strategy. Thus FlexPop has potential in the use of ICN for the future Internet such as in deployment of the IoT technology

A Review on Cache Replacement Strategies in Named Data Network

Named Data Network (NDN) architecture is one of the newest and future-aspired Internet communication systems. Video-on-Demand (VoD) has rapidly emerged as a popular online service. However, it is costly, considering its high bandwidth and popularity. Internet on-demand video traffic has been growing quite fast, and on-demand video streaming has gained much attention. The problem of this study is that the NDN architecture is processing several forms of online video requests simultaneously. However, limited cache and multiple buffering of requested videos result in loss of data packet as a consequence of the congestion in the cache storage network. Addressing this problem is essential as congestion cause network instability. This work emphasizes on the review of cache replacement strategies to deal with the congestion issue in Named Data Networks (NDN) during the VoD delivery in order to determine the performance (strengths and weaknesses) of the cache replacement strategies. Finally, this study proposes the replacement strategies must be enhanced with a new strategy that depends on popularity and priority regarding the congestion. This study would positively benefits both suppliers and users of Internet videos

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

Exploiting Natural On-chip Redundancy for Energy Efficient Memory and Computing

Power density is currently the primary design constraint across most computing segments and the main performance limiting factor. For years, industry has kept power density constant, while increasing frequency, lowering transistors supply (Vdd) and threshold (Vth) voltages. However, Vth scaling has stopped because leakage current is exponentially related to it. Transistor count and integration density keep doubling every process generation (Moore’s Law), but the power budget caps the amount of hardware that can be active at the same time, leading to dark silicon. With each new generation, there are more resources available, but we cannot fully exploit their performance potential. In the last years, different research trends have explored how to cope with dark silicon and unlock the energy efficiency of the chips, including Near-Threshold voltage Computing (NTC) and approximate computing. NTC aggressively lowers Vdd to values near Vth. This allows a substantial reduction in power, as dynamic power scales quadratically with supply voltage. The resultant power reduction could be used to activate more chip resources and potentially achieve performance improvements. Unfortunately, Vdd scaling is limited by the tight functionality margins of on-chip SRAM transistors. When scaling Vdd down to values near-threshold, manufacture-induced parameter variations affect the functionality of SRAM cells, which eventually become not reliable. A large amount of emerging applications, on the other hand, features an intrinsic error-resilience property, tolerating a certain amount of noise. In this context, approximate computing takes advantage of this observation and exploits the gap between the level of accuracy required by the application and the level of accuracy given by the computation, providing that reducing the accuracy translates into an energy gain. However, deciding which instructions and data and which techniques are best suited for approximation still poses a major challenge. This dissertation contributes in these two directions. First, it proposes a new approach to mitigate the impact of SRAM failures due to parameter variation for effective operation at ultra-low voltages. We identify two levels of natural on-chip redundancy: cache level and content level. The first arises because of the replication of blocks in multi-level cache hierarchies. We exploit this redundancy with a cache management policy that allocates blocks to entries taking into account the nature of the cache entry and the use pattern of the block. This policy obtains performance improvements between 2% and 34%, with respect to block disabling, a technique with similar complexity, incurring no additional storage overhead. The latter (content level redundancy) arises because of the redundancy of data in real world applications. We exploit this redundancy compressing cache blocks to fit them in partially functional cache entries. At the cost of a slight overhead increase, we can obtain performance within 2% of that obtained when the cache is built with fault-free cells, even if more than 90% of the cache entries have at least a faulty cell. Then, we analyze how the intrinsic noise tolerance of emerging applications can be exploited to design an approximate Instruction Set Architecture (ISA). Exploiting the ISA redundancy, we explore a set of techniques to approximate the execution of instructions across a set of emerging applications, pointing out the potential of reducing the complexity of the ISA, and the trade-offs of the approach. In a proof-of-concept implementation, the ISA is shrunk in two dimensions: Breadth (i.e., simplifying instructions) and Depth (i.e., dropping instructions). This proof-of-concept shows that energy can be reduced on average 20.6% at around 14.9% accuracy loss

Elastic Highly Available Cloud Computing

High availability and elasticity are two the cloud computing services technical features. Elasticity is a key feature of cloud computing where provisioning of resources is closely tied to the runtime demand. High availability assure that cloud applications are resilient to failures. Existing cloud solutions focus on providing both features at the level of the virtual resource through virtual machines by managing their restart, addition, and removal as needed. These existing solutions map applications to a specific design, which is not suitable for many applications especially virtualized telecommunication applications that are required to meet carrier grade standards. Carrier grade applications typically rely on the underlying platform to manage their availability by monitoring heartbeats, executing recoveries, and attempting repairs to bring the system back to normal. Migrating such applications to the cloud can be particularly challenging, especially if the elasticity policies target the application only, without considering the underlying platform contributing to its high availability (HA). In this thesis, a Network Function Virtualization (NFV) framework is introduced; the challenges and requirements of its use in mobile networks are discussed. In particular, an architecture for NFV framework entities in the virtual environment is proposed. In order to reduce signaling traffic congestion and achieve better performance, a criterion to bundle multiple functions of virtualized evolved packet-core in a single physical device or a group of adjacent devices is proposed. The analysis shows that the proposed grouping can reduce the network control traffic by 70 percent. Moreover, a comprehensive framework for the elasticity of highly available applications that considers the elastic deployment of the platform and the HA placement of the application’s components is proposed. The approach is applied to an internet protocol multimedia subsystem (IMS) application and demonstrate how, within a matter of seconds, the IMS application can be scaled up while maintaining its HA status