Reduce Storage Overhead Research Articles

Malware categorization using dynamic mnemonic frequency analysis with redundancy filtering

The battle between malware developers and security analysts continues, and the number of malware and malware variants keeps increasing every year. Automated malware generation tools and various detection evasion techniques are also developed every year. To catch up with the advance of malware development technologies, malware analysis techniques need to be advanced to help security analysts. In this paper, we propose a malware analysis method to categorize malware using dynamic mnemonic frequencies. We also proposed a redundancy filtering technique to alleviate drawbacks of dynamic analysis. Experimental results show that our proposed method can categorize malware and can reduce storage overheads of dynamic analysis.

Beyond graphs

Graph theory has provided a powerful modeling foundation for problems in many domains, but we argue that group interactions are better modeled by hypergraphs. As we work toward scalable systems for such hypergraph analysis, several major challenges and opportunities arise; here we highlight a sample of those challenges. We consider the need for efficient representations of hypergraphs, and show that in some cases it is possible to exploit the specific structure of a hypergraph to reduce storage overhead. We also explore several challenges in distributing computation on hypergraphs, including the need for more general partitioning approaches. Finally, we discuss several other problems that arise as we move from graphs to hypergraphs, including designing programming models, using hypergraphs to model real-world groups, and the need for a better understanding of the structural characteristics of hypergraphs.

Two-Layer Error Control Codes Combining Rectangular and Hamming Product Codes for Cache Error

We propose a novel two-layer error control code, combining error detection capability of rectangular codes and error correction capability of Hamming product codes in an efficient way, in order to increase cache error resilience for many core systems, while maintaining low power, area and latency overhead. Based on the fact of low latency and overhead of rectangular codes and high error control capability of Hamming product codes, two-layer error control codes employ simple rectangular codes for each cache line to detect cache errors, while loading the extra Hamming product code checks bits in the case of error detection; thus enabling reliable large-scale cache operations. Analysis and experiments are conducted to evaluate the cache fault-tolerant capability of various existing solutions and the proposed approach. The results show that the proposed approach can significantly increase Mean-Error-To-Failure (METF) and Mean-Time-To-failure (MTTF) up to 2.8×, reduce storage overhead by over 57%, and increase instruction per-cycle (IPC) up to 7%, compared to complex four-way 4EC5ED; and it increases METF and MTTF up to 133×, reduces storage overhead by over 11%, and achieves a similar IPC compared to simple eight-way single-error correcting double-error detecting (SECDED). The cost of the proposed approach is no more than 4% external memory access overhead.

Privacy-Assured Outsourcing of Image Reconstruction Service in Cloud

Large-scale image data sets are being exponentially generated today. Along with such data explosion is the fast-growing trend to outsource the image management systems to the cloud for its abundant computing resources and benefits. How to protect the sensitive data while enabling outsourced image services, however, becomes a major concern. To address these challenges, we propose outsourced image recovery service (OIRS), a novel outsourced image recovery service architecture, which exploits different domain technologies and takes security, efficiency, and design complexity into consideration from the very beginning of the service flow. Specifically, we choose to design OIRS under the compressed sensing framework, which is known for its simplicity of unifying the traditional sampling and compression for image acquisition. Data owners only need to outsource compressed image samples to cloud for reduced storage overhead. In addition, in OIRS, data users can harness the cloud to securely reconstruct images without revealing information from either the compressed image samples or the underlying image content. We start with the OIRS design for sparse data, which is the typical application scenario for compressed sensing, and then show its natural extension to the general data for meaningful tradeoffs between efficiency and accuracy. We thoroughly analyze the privacy-protection of OIRS and conduct extensive experiments to demonstrate the system effectiveness and efficiency. For completeness, we also discuss the expected performance speedup of OIRS through hardware built-in system design.

Experiences with evacuation route planning algorithms

Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in the event of natural disasters. Hurricane Rita and the recent tsunami revealed limitations of traditional approaches to provide emergency preparedness for evacuees and to predict the effects of evacuation route planning (ERP). Challenges arise during evacuations due to the spread of people over space and time and the multiple paths that can be taken to reach them; key assumptions such as stationary ranking of alternative routes and optimal substructure are violated in such situations. Algorithms for ERP were first developed by researchers in operations research and transportation science. However, these proved to have high computational complexity and did not scale well to large problems. Over the last decade, we developed a different approach, namely the Capacity Constrained Route Planner (CCRP), which generalizes shortest path algorithms by honoring capacity constraints and the spread of people over space and time. The CCRP uses time-aggregated graphs to reduce storage overhead and increase computational efficiency. Experimental evaluation and field use in Twin Cities Homeland Security scenarios demonstrated that CCRP is faster, more scalable, and easier to use than previous techniques. We also propose a novel scalable algorithm that exploits the spatial structure of transportation networks to accelerate routing algorithms for large network datasets. We evaluated our new approach for large-scale networks around downtown Minneapolis and riverside areas. This article summarizes experiences and lessons learned during the last decade in ERP and relates these to Professor Goodchild's contributions.

Feasibility of position-based multivariate cryptosystems for WSN

In this research paper, the positions of sensor nodes are used for generating their public keys. In a wireless sensor networks (WSN), the nodes observe the environment in their vicinity and send the position specific data to the sink. Since the position and sensed data of a sensor are linked up to each other, the sensor position can be used as its unique public key. Multivariate cryptography offers a lightweight alternative to public key cryptography for sensor networks and position as public key can enhance its viability for WSN. In this study, we proposed a position-based multivariate cryptosystem for WSN that verifies the position of the sensor node and also prevented from the collusion attack. The performance and security analysis of the proposed scheme demonstrates that reduce storage overhead, communication overhead and prevent collusion attack in various contexts are possible within lightweight computational constraints.

A Replication Detection scheme for Sensor Networks

In this paper, we propose a replication detection scheme based on cluster-based structure. Our scheme employs the authenticated claim, neighbor proof and travel-time as the basic means of constructing replication detection procedure in order to avoid the need for public key cryptography and reduce storage overhead. Our scheme can effectively detect replica nodes and prevent replica nodes from adding sensor networks. Moreover, our scheme has no use for periodical replica detection and greatly decreases communication overhead. The analysis of security and performance shows that our scheme not only can resist node replication attack, but also has low computation overhead and smaller storage overhead.

SGKP: Self Configured Group based Key Predistribution for Reducing Storage Overhead in Key Establishment for Wireless Sensor Networks

SGKP: Self Configured Group based Key Predistribution for Reducing Storage Overhead in Key Establishment for Wireless Sensor Networks

CCNoC: Cache-Coherent Network on Chip for Chip Multiprocessors

As the number of cores in chip multiprocessors (CMPs) increases, cache coherence protocol has become a key issue in integration of chip multiprocessors. Supporting cache coherence protocol in large chip multiprocessors still faces three hurdles: design complexity, performance and scalability. This paper proposes Cache Coherent Network on Chip (CCNoC), a scheme that decouples cache coherency maintenance from processors and shared L2 caches and implements it completely in network on chip to free up processors and shared L2 caches from the chore of maintaining coherency, thereby reduces design complexity of CMPs. In this way, CCNoC also improves the performance of cache coherence protocol through reducing directory access latency and enhances scalability by avoiding massive directories overhead in shared L2 caches. In CCNoC, coherence state caches and active directory caches are implemented in the network interface components of network on chip to maintain cache coherence states for blocks in L1 caches and manage directory information for recently accessed blocks in L2 caches respectively. CCNoC provides a scalable CMP framework to tackle cache coherency which is the foundation of CMP. This paper evaluates the performance of CCNoC. Experimental results show that for a 16-core system, CCNoC improves performance by 3% on average over the conventional chip multiprocessor and by 10% at best, while reduces storage overhead by 1.8% and saves directory storage by 88%, showing good scalability.

Distributed pattern matching: a key to flexible and efficient P2P search

Flexibility and efficiency are the prime requirements for any P2P search mechanism. Existing P2P systems do not provide satisfactory solution for achieving these two conflicting goals. Unstructured search protocols (as adopted in Gnutella and FastTrack) provide search flexibility but exhibit poor performance characteristics. Structured search techniques (mostly Distributed Hash Table (DHT)-based), on the other hand, can efficiently route queries but support exact-match semantic only. In this paper we have defined Distributed Pattern Matching (DPM) problem and have presented a novel P2P architecture, named Distributed Pattern Matching System (DPMS), as a solution. Possible application areas of DPM include P2P search, service discovery and P2P databases. In DPMS, advertised patterns are replicated and aggregated by the peers, organized in a lattice-like hierarchy. Replication Improves availability and resilience to peer failure, and aggregation reduces storage overhead. An advertised pattern can be discovered using any subset of its 1-bits. Search complexity in DPMS is logarithmic to the total number of peers in the system. Advertisement overhead and guarantee on search completeness is comparable to that of DHT-based systems. We have presented mathematical analysis and simulation results to demonstrate the effectiveness of DPMS.

Improving duplicate elimination in storage systems

Minimizing the amount of data that must be stored and managed is a key goal for any storage architecture that purports to be scalable. One way to achieve this goal is to avoid maintaining duplicate copies of the same data. Eliminating redundant data at the source by not writing data which has already been stored not only reduces storage overheads, but can also improve bandwidth utilization. For these reasons, in the face of today's exponentially growing data volumes, redundant data elimination techniques have assumed critical significance in the design of modern storage systems.Intelligent object partitioning techniques identify data that is new when objects are updated, and transfer only these chunks to a storage server. In this article, we propose a new object partitioning technique, called fingerdiff , that improves upon existing schemes in several important respects. Most notably, fingerdiff dynamically chooses a partitioning strategy for a data object based on its similarities with previously stored objects in order to improve storage and bandwidth utilization. We present a detailed evaluation of fingerdiff , and other existing object partitioning schemes, using a set of real-world workloads. We show that for these workloads, the duplicate elimination strategies employed by fingerdiff improve storage utilization on average by 25%, and bandwidth utilization on average by 40% over comparable techniques.

A Fault-Tolerant Multicast Routing Protocol for Mobile Ad-Hoc Networks

In this work, we propose an efficient multicast routing protocol for mobile ad hoc networks. To achieve high efficiency with low channel and storage overhead, the proposed protocol employs the following mechanism: (1) on-demand invocation of route setup and route maintenance process to avoid periodical control packet transmissions, thus reducing channel overhead, (2) creation of “forwarding group” to forward multicast packets, thus reducing storage overhead, (3) exploration of multiple possible routes from a single flooded query to reduce the frequency of route discovery, thus further reducing channel overhead, (4) a new route setup mechanism that allows a newly joining node to find the nearest forwarding node to minimize the number of added forwarding nodes, thus further reducing storage overhead. To provide the capability of fault tolerance, we introduce the alternate route together with the primary route. We observe that for multicasting the channel and storage overheads of the presented approach are less than those of the DVMRP approach. Also, the channel overhead is less than that in the FGMP approach for multicasting in low mobility scenario, while the storage overheads are the same in the presented approach and in the FGMP approach.

Selective, accurate, and timely self-invalidation using last-touch prediction

Communication in cache-coherent distributed shared memory (DSM) often requires invalidating (or writing back) cached copies of a memory block, incurring high overheads. This paper proposes Last-Touch Predictors (LTPs) that learn and predict the “last touch” to a memory block by one processor before the block is accessed and subsequently invalidated by another. By predicting a last-touch and (self-)invalidating the block in advance, an LTP hides the invalidation time, significantly reducing the coherence overhead. The key behind accurate last-touch prediction is trace-based correlation, associating a last-touch with the sequence of instructions (i.e., a trace) touching the block from a coherence miss until the block is invalidated. Correlating instructions enables an LTP to identify a last-touch to a memory block uniquely throughout an application's execution. In this paper, we use results from running shared-memory applications on a simulated DSM to evaluate LTPs. The results indicate that: (1) our base case LTP design, maintaining trace signatures on a per-block basis, substantially improves prediction accuracy over previous self-invalidation schemes to an average of 79%; (2) our alternative LTP design, maintaining a global trace signature table, reduces storage overhead but only achieves an average accuracy of 58%; (3) last-touch prediction based on a single instruction only achieves an average accuracy of 41% due to instruction reuse within and across computation; and (4) LTP enables selective, accurate, and timely self-invalidation in DSM, speeding up program execution on average by 11%.

Sugarcane Production, Processing and Marketing in Tanzania

Sugarcane is one of the important food and commercial crops of Tanzania. Its production is concentrated mainly in three regions, Morogoro, Kagera and Kilimanjaro. Most of the sugar produced in the country is for home consumption and only a small proportion is exported to service foreign debts. This paper briefly reviews the agronomic, production and marketing aspects of sugarcane in Tanzania, with special emphasis on the factors associated with variation in production during the past ten years. During the 1983/84 season, the country produced slightly over 130,000 tons of processed sugar, but 1988/89 production had dropped to just about 96,000 tons. During the 1990’s (1991/92-1993/94), production increased in response to the trade liberalization policy of the country. To increase and sustain the country’s future sugar production, improved soil management of sugarcane fields, irrigation technology and the use of improved clones need to be introduced. The current marketing and handling structure in the sugar industry requires reform in order to increase efficiency and reduce storage overheads paid by the consumers.

On the Application of the Minimum Degree Algorithm to Finite Element Systems

We describe an efficient implementation of the so-called minimum degree algorithm, which experience has shown to produce efficient orderings for sparse positive definite systems. Our algorithm is a modification of the original, tailored to finite element problems, and is shown to induce a partitioning in a natural way. The partitioning is then refined so as to significantly reduce the number of non-null off-diagonal blocks. This refinement is important in practical terms because it reduces storage overhead in our linear equation solver, which utilizes the ordering and partitioning produced by our algorithm. Finally, we provide some numerical experiments comparing our ordering/solver package to more conventional band-oriented packages.