Khin Mi Mi Aung

A loss-free multipathing solution for data center network using software-defined networking approach

Conventional Ethernet protocols struggle to meet the scalability and performance requirements of data centers. Viable replacements have been proposed for Data Center Ethernet (DCE): link-layer multipathing (MP) is deployed to replace Spanning Tree Protocol (STP) and thus improves network throughput; end-to-end link-layer congestion control (CC) is proposed to better guarantee loss-free frame delivery for Ethernet. However, little work has been done to incorporate MP and CC to offer a more comprehensive solution for DCE. In this paper, we propose a two-tier solution by integrating our Dynamic Load Balancing MultiPath (DLBMP) scheme with CC. Instead of using two separate parameters, i.e. path load and buffer level, to trigger MP and CC, our solution only needs to monitor path load metric to manage MP and CC in an integrated way. Different from a pure CC mechanism, which generates notifications from network core, our integrated CC can make use of link load information in edge switches which directly inform sources to control traffic admission. To minimize overhead and accelerate update, Software-Defined Networking (SDN) techniques are employed in our implementation, which decouples routing intelligence from data transmission. Hence, data sources can react more rapidly to congestions and network can be guaranteed with loss-free delivery. In addition, our MP scheme is further improved by introducing application-layer flow differentiation. With such a fine flow differentiation (FFD) mechanism, traffic can be more evenly distributed along multipaths, resulting in better bandwidth utilization. Simulation results show that our combined solution can further improve network throughput with FFD mechanism and guarantee loss-free delivery with integrated CC.

An efficient flow cache algorithm with improved fairness in Software-Defined Data Center Networks

The use of Software-Defined Networking (SDN) with OpenFlow-enabled switches in Data Centers has received much attention from researchers and industries. One of the major issues in OpenFlow switch is the limited size of the flow table resulting in evictions of flows from the flow table. From Data Center traffic characteristics, we observe that elephant flows are very large in size (data volume) but few in numbers when compared to mice flows. Thus, Elephant flows are more likely to be evicted, due to the limited size of the switch flow table causing additional traffic to the controller. We propose a differential flow cache framework that achieves fairness and efficient cache maintenance with fast lookup and reduced cache miss ratio. The framework uses a hash-based placement and localized Least Recently Used (LRU)-based replacement mechanisms.

Building a large-scale object-based active storage platform for data analytics in the internet of things

Due to consistent improvements in memory and processor technology, object storage devices (OSDs) have greater memory space and more powerful processing power, which allow the OSDs to execute user-defined programs. Shifting part of an application’s processing to the disk drives drops the amount of data transferred across the network and explores the parallelism of large-scale distributed storage systems, reducing the execution time for many basic data analytics tasks. In this paper, we propose a large-scale object-based active storage platform, named Gem, for data analytics in the internet of things (IoT). All data from the IoT that resides in disk drives form objects with attributes, methods and policies. For some applications such as data analytics, application-specific operations are executed by the drive processors. In this way, only the results are returned to clients, rather than data files being read by the clients. Therefore, the platform Gem is able to greatly reduce the overhead of data analytics applications in the Internet of Things. By conducting performance evaluation, experimental results demonstrate the effectiveness and scalability of Gem.

Power-Efficient Virtual Machine Placement and Migration in Data Centers

In this paper, we propose a power-efficient solution for virtual machine placement and migration in a fat tree data center network. This solution reduces power consumption as well as job delay by aggregating virtual machines to a few hyper visors and migrating communicating parties to close locations. In this work, we consider OpenFlow as the implementation protocol. In an OpenFlow environment, a centralized controller oversees job loads, virtual machine requirements and hardware availability. Given observation of such global knowledge, the OpenFlow controller can schedule jobs and distribute virtual machines accordingly. As jobs change and flows shift, the OpenFlow controller dynamically adjusts virtual machine assignments by aggregating virtual machines to close locations in order to save energy. With this placement and migration proposal, more jobs can operate concurrently with close sources and destinations of flows, thus both job and flow delay can be reduced.

Secure searching on cloud storage enhanced by homomorphic indexing

Enterprise cloud tenants would store their outsourced cloud data in encrypted form for data privacy and security. However, flexible data access functions such as data searching is usually sacrificed as a result. Thus, enterprise tenants demand secure data retrieval and computation solution from the cloud provider, which will allow them to utilize cloud services without the risks of leaking private data to outsiders and even service providers.In this paper, we propose an exclusive-or (XOR) homomorphism encryption scheme to support secure keyword searching on encrypted data for cloud storage. First, this scheme specifies a new data protection method by encrypting the keyword and randomizing it by performing XOR operation with a random bit-string for each session to protect access pattern leakage; Secondly, the homomorphic evaluation key enables the searching evaluation to be on-demand calculated, thus it removes the dependency of key storage on cloud and enhance protection against clouds violability; Thirdly, this scheme can effectively protect data-in-transit against passive attack such as access pattern analysis due to the randomization. This scheme also can reduce data leakage to service provider because the homomorphism-key solution instead of key storage on cloud. The above three features have been proved by the experiments and further tested out at Email service which can support secure subject searching. The execution time of one searching process is just in the order of milliseconds. We could get 2-3 times speedup compared to default utility grep with the concern of expensive one-time indexing which can be built off-line in advance. A searchable encryption is presented against both data and access pattern leakage.A homomorphic exclusive-or (XOR) function is defined to enable the evaluation key to be calculated instead of storing.An effective and feasible approach performs with a query of less than 60 milliseconds among 100,000 entries.

ESPRESSO: An Encryption as a Service for Cloud Storage Systems

Cloud storage systems have become the primary storage space for cloud users’ data. Despite the huge advantages and flexibility of the cloud storage services, many challenges are hindering the migration of users’ data into the cloud. Among them, the data privacy needs to be considered. In this paper, we design and implement an encryption service namely ESPRESSO (Encryption as a Service for Cloud Storage Systems) to protect the users’ data by using advanced encryption algorithms. The flexible design and the standalone property of ESPRESSO allow cloud storage service providers to easily integrate it without heavy modification and implementation of their infrastructures. ESPRESSO was integrated into two open-source cloud storage platforms: OpenStack/Swift and Nimbus/Cumulus. The real experiments were conducted, and the results assess the performance and effectiveness of ESPRESSO.

Dynamic Load Balancing Multipathing in Data Center Ethernet

Currently implemented Spanning Tree Protocol (STP) cannot meet the requirement of a data center due to its poor bandwidth utilization and lack of multipathing capability. In this paper, we propose a layer-2 multipathing solution, namely dynamic load balancing multipathing (DLBMP), for data center Ethernets. With DLBMP, traffic between two communication nodes can be spread among multiple paths. The traffic load of all paths is continuously monitored so that traffic split to each path can be dynamically adjusted. In addition, per-flow forwarding is preserved to guarantee in-order frame delivery. Computer simulations show that DLBMP gives much better performance as compared to STP due to its multipathing and dynamic load balancing capability.

A layer2 multipath solution and its performance evaluation for Data Center Ethernets

Data Center Ethernet is likely to be deployed as the communication infrastructure for future data centers, which carries multiple types of traffic with very different characteristics and handling requirements. Conventional Spanning Tree Protocol STP cannot meet the requirement of a Data Center Ethernet framework because of its poor bandwidth utilization and lack of multipathing capability. In this paper, we propose a layer2 multipathing solution, namely optimized dynamic load-balancing multipathing ODLBMP, to be deployed in Data Center Ethernet. Our proposed method utilizes all available links and ports for frame delivery and can split traffic of a communication pair along multiple paths. In ODLBMP, the traffic loads of all paths are continuously monitored so that traffic assigned to each path can be dynamically adjusted to avoid path/link over-utilization. Per-flow forwarding is observed in ODLBMP to guarantee the in-order delivery, which is important for most storage traffic. In addition, ODLBMP finely differentiates flows from application perspective so it has more flexibility in traffic splitting and route selection, and achieves better multipath load balancing. Computer simulations show that our proposed algorithm performs better than other compared algorithms, including STP, Transparent Interconnection of Lots of Links, and DLBMP, in all simulation scenarios in terms of frame delivery ratio and network throughput. Copyright

Incorporating Energy and Throughput Awareness in Design Space Exploration and Run-Time Mapping for Heterogeneous MPSoCs

The advancement in process technology has enabled integration of different types of processing cores into a single chip towards creating heterogeneous Multiprocessor Systems-on-Chip (MPSoCs). While providing high level of computation power to support complex applications, these modern systems also introduce novel challenges for system designers, like managing a huge number of mappings (application tasks to processing cores allocations) that increases exponentially with the number of cores and their types. This paper presents a mapping approach that computes multiple energy-throughput trade-off points (mappings) at design-time and uses one of these points at run-time based on desired throughput and current resource availability while optimizing for the overall energy consumption. While significantly reducing the complexity of the design space exploration (DSE) to compute mappings at design-time, the proposed strategy still evaluates mappings for all the resource combinations of the platform, providing efficient mapping solutions for all the scenarios of system architecture at run-time. Moreover, the proposed approach performs energy-aware mapping at run-time while utilizing the DSE results. Experimental results show that proposed strategy achieves better energy-throughput trade-off points, covers all the resource combinations and reduces energy consumption up to 24.93% at design-time and additionally 17.8% at run-time when compared to state-of-the-art techniques.

Energy optimizations for data center network: Formulation and its solution

Data center consumes increasing amount of power nowadays, together with expanding number of data centers and upgrading data center scale, its power consumption becomes a knotty issue. While main efforts of this research focus on server and storage power reduction, network devices as part of the key components of data centers, also contribute to the overall power consumption as data centers expand. In this paper, we address this problem with two perspectives. First, in a macro level, we attempt to reduce redundant energy usage incurred by network redundancies for load balancing. Second, in the micro level, we design algorithm to limit port rate in order to reduce unnecessary power consumption. Given the guidelines we obtained from problem formulation, we propose a solution based on greedy approach with integration of network traffic and minimization of switch link rate. We also present results from a simulation-based performance evaluation which shows that expected power saving is achieved with tolerable delay.