Efficient quality of service‐aware packet chunking scheme for machine‐to‐machine cloud services

With the recent advances in machine-to-machine(M2M) communications, huge numbers of devices have become connected and massive amounts of traffic are exchanged. M2M applications typically generate small packets, which can profoundly affect the network performance. Namely, even if the packet arrival rate at the router is lower than the link bandwidth, bits per second(BPS), it can exceed the router forwarding capacity, which indicates the maximum number of forwarded packets per second(PPS). This will cause the decrease in the network throughput. Therefore, eliminating the PPS limitation by chunking small packets will enable M2M cloud services to spread further. This paper proposes new packet-chunking schemes aimed at meeting both application requirements and improving achievable router throughput. In our schemes, multiple buffers, each of which accommodates packets classified based on their delay requirement, are installed in parallel. Herein, we report on analysis of the theoretically performance of these schemes, which enabled us to derive some important features. We also propose a scheme whereby a single chunking buffer and parallel multiple buffers were arranged in tandem. Through our simulation and numerical results, we determined that these schemes provide excellent performance in reducing the number of outgoing packets from the router while meeting various delay requirements.The 2nd IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (IEEE HPCA 2016),March 12, 2016, Barcelona, Spai

Doctor of Philosophy

dissertationIn the past few years, we have seen a tremendous increase in digital data being generated. By 2011, storage vendors had shipped 905 PB of purpose-built backup appliances. By 2013, the number of objects stored in Amazon S3 had reached 2 trillion. Facebook had stored 20 PB of photos by 2010. All of these require an efficient storage solution. To improve space efficiency, compression and deduplication are being widely used. Compression works by identifying repeated strings and replacing them with more compact encodings while deduplication partitions data into fixed-size or variable-size chunks and removes duplicate blocks. While we have seen great improvements in space efficiency from these two approaches, there are still some limitations. First, traditional compressors are limited in their ability to detect redundancy across a large range since they search for redundant data in a fine-grain level (string level). For deduplication, metadata embedded in an input file changes more frequently, and this introduces more unnecessary unique chunks, leading to poor deduplication. Cloud storage systems suffer from unpredictable and inefficient performance because of interference among different types of workloads. This dissertation proposes techniques to improve the effectiveness of traditional compressors and deduplication in improving space efficiency, and a new IO scheduling algorithm to improve performance predictability and efficiency for cloud storage systems. The common idea is to utilize similarity. To improve the effectiveness of compression and deduplication, similarity in content is used to transform an input file into a compression- or deduplication-friendly format. We propose Migratory Compression, a generic data transformation that identifies similar data in a coarse-grain level (block level) and then groups similar blocks together. It can be used as a preprocessing stage for any traditional compressor. We find metadata have a huge impact in reducing the benefit of deduplication. To isolate the impact from metadata, we propose to separate metadata from data. Three approaches are presented for use cases with different constrains. For the commonly used tar format, we propose Migratory Tar: a data transformation and also a new tar format that deduplicates better. We also present a case study where we use deduplication to reduce storage consumption for storing disk images, while at the same time achieving high performance in image deployment. Finally, we apply the same principle of utilizing similarity in IO scheduling to prevent interference between random and sequential workloads, leading to efficient, consistent, and predictable performance for sequential workloads and a high disk utilization

Improving Data Management and Data Movement Efficiency in Hybrid Storage Systems

University of Minnesota Ph.D. dissertation.July 2017. Major: Computer Science. Advisor: David Du. 1 computer file (PDF); ix, 116 pages.In the big data era, large volumes of data being continuously generated drive the emergence of high performance large capacity storage systems. To reduce the total cost of ownership, storage systems are built in a more composite way with many different types of emerging storage technologies/devices including Storage Class Memory (SCM), Solid State Drives (SSD), Shingle Magnetic Recording (SMR), Hard Disk Drives (HDD), and even across off-premise cloud storage. To make better utilization of each type of storage, industries have provided multi-tier storage through dynamically placing hot data in the faster tiers and cold data in the slower tiers. Data movement happens between devices on one single device and as well as between devices connected via various networks. Toward improving data management and data movement efficiency in such hybrid storage systems, this work makes the following contributions: To bridge the giant semantic gap between applications and modern storage systems, passing a piece of tiny and useful information (I/O access hints) from upper layers to the block storage layer may greatly improve application performance or ease data management in heterogeneous storage systems. We present and develop a generic and flexible framework, called HintStor, to execute and evaluate various I/O access hints on heterogeneous storage systems with minor modifications to the kernel and applications. The design of HintStor contains a new application/user level interface, a file system plugin and a block storage data manager. With HintStor, storage systems composed of various storage devices can perform pre-devised data placement, space reallocation and data migration polices assisted by the added access hints. Each storage device/technology has its own unique price-performance tradeoffs and idiosyncrasies with respect to workload characteristics they prefer to support. To explore the internal access patterns and thus efficiently place data on storage systems with fully connected (i.e., data can move from one device to any other device instead of moving tier by tier) differential pools (each pool consists of storage devices of a particular type), we propose a chunk-level storage-aware workload analyzer framework, simplified as ChewAnalyzer. With ChewAnalzyer, the storage manager can adequately distribute and move the data chunks across different storage pools. To reduce the duplicate content transferred between local storage devices and devices in remote data centers, an inline Network Redundancy Elimination (NRE) process with Content-Defined Chunking (CDC) policy can obtain a higher Redundancy Elimination (RE) ratio but may suffer from a considerably higher computational requirement than fixed-size chunking. We build an inline NRE appliance which incorporates an improved FPGA based scheme to speed up CDC processing. To efficiently utilize the hardware resources, the whole NRE process is handled by a Virtualized NRE (VNRE) controller. The uniqueness of this VNRE that we developed lies in its ability to exploit the redundancy patterns of different TCP flows and customize the chunking process to achieve a higher RE ratio

Network Traffic Measurements, Applications to Internet Services and Security

The Internet has become along the years a pervasive network interconnecting billions of users and is now playing the role of collector for a multitude of tasks, ranging from professional activities to personal interactions. From a technical standpoint, novel architectures, e.g., cloud-based services and content delivery networks, innovative devices, e.g., smartphones and connected wearables, and security threats, e.g., DDoS attacks, are posing new challenges in understanding network dynamics. In such complex scenario, network measurements play a central role to guide traffic management, improve network design, and evaluate application requirements. In addition, increasing importance is devoted to the quality of experience provided to final users, which requires thorough investigations on both the transport network and the design of Internet services. In this thesis, we stress the importance of users’ centrality by focusing on the traffic they exchange with the network. To do so, we design methodologies complementing passive and active measurements, as well as post-processing techniques belonging to the machine learning and statistics domains. Traffic exchanged by Internet users can be classified in three macro-groups: (i) Outbound, produced by users’ devices and pushed to the network; (ii) unsolicited, part of malicious attacks threatening users’ security; and (iii) inbound, directed to users’ devices and retrieved from remote servers. For each of the above categories, we address specific research topics consisting in the benchmarking of personal cloud storage services, the automatic identification of Internet threats, and the assessment of quality of experience in the Web domain, respectively. Results comprise several contributions in the scope of each research topic. In short, they shed light on (i) the interplay among design choices of cloud storage services, which severely impact the performance provided to end users; (ii) the feasibility of designing a general purpose classifier to detect malicious attacks, without chasing threat specificities; and (iii) the relevance of appropriate means to evaluate the perceived quality of Web pages delivery, strengthening the need of users’ feedbacks for a factual assessment

HTTP 1.2: DISTRIBUTED HTTP FOR LOAD BALANCING SERVER SYSTEMS

Content hosted on the Internet must appear robust and reliable to clients relying on such content. As more clients come to rely on content from a source, that source can be subjected to high levels of load. There are a number of solutions, collectively called load balancers, which try to solve the load problem through various means. All of these solutions are workarounds for dealing with problems inherent in the medium by which content is served thereby limiting their effectiveness. HTTP, or Hypertext Transport Protocol, is the dominant mechanism behind hosting content on the Internet through websites. The entirety of the Internet has changed drastically over its history, with the invention of new protocols, distribution methods, and technological improvements. However, HTTP has undergone only three versions since its inception in 1991, and all three versions serve content as a text stream that cannot be interrupted to allow for load balancing decisions. We propose a solution that takes existing portions of HTTP, augments them, and includes some new features in order to increase usability and management of serving content over the Internet by allowing redirection of content in-stream. This in-stream redirection introduces a new step into the client-server connection where servers can make decisions while continuing to serve content to the client. Load balancing methods can then use the new version of HTTP to make better decisions when applied to multi-server systems making load balancing more robust, with more control over the client-server interaction

Network and Server Resource Management Strategies for Data Centre Infrastructures: A Survey

The advent of virtualisation and the increasing demand for outsourced, elastic compute charged on a pay-as-you-use basis has stimulated the development of large-scale Cloud Data Centres (DCs) housing tens of thousands of computer clusters. Of the signi�cant capital outlay required for building and operating such infrastructures, server and network equipment account for 45% and 15% of the total cost, respectively, making resource utilisation e�ciency paramount in order to increase the operators' Return-on-Investment (RoI). In this paper, we present an extensive survey on the management of server and network resources over virtualised Cloud DC infrastructures, highlighting key concepts and results, and critically discussing their limitations and implications for future research opportunities. We highlight the need for and bene �ts of adaptive resource provisioning that alleviates reliance on static utilisation prediction models and exploits direct measurement of resource utilisation on servers and network nodes. Coupling such distributed measurement with logically-centralised Software De�ned Networking (SDN) principles, we subsequently discuss the challenges and opportunities for converged resource management over converged ICT environments, through unifying control loops to globally orchestrate adaptive and load-sensitive resource provisioning

Enabling the “Easy Button” for Broad, Parallel Optimization of Functions Evaluated by Simulation

Java Optimization by Simulation (JOBS) is presented: an open-source, object-oriented Java library designed to enable the study, research, and use of optimization for models evaluated by simulation. JOBS includes several novel design features that make it easy for a simulation modeler, without extensive expertise in optimization or parallel computation, to define an optimization model with deterministic and/or stochastic constraints, choose one or more metaheuristics to solve it and run, using massively parallel function evaluation to reduce wall-clock times. JOBS is supported by a new language independent, application programming interface (API) for remote simulation model evaluation and a serverless computing environment to provide massively parallel function evaluation, on demand. Dynamic loop scheduling methods are evaluated in the serverless environment with the opportunity for significant resource contention for master node computing power and network bandwidth. JOBS implements several population-based and single-solution improvement metaheuristics (solvers) for real, discrete, and mixed problems. The object-oriented design is extendible with classes that drastically reduce the amount of code required to implement a new solver and encourage re-use of solvers as building blocks for creating new multi-stage solvers or memetic algorithms

On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage. In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer

Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics