羟氨苄青霉素引起大疱性类天疱疮样疹

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

http://www.acsu.buffalo.edu/~jmjornet/papers/2014/j1.pdf

Full length article: Terahertz band: Next frontier for wireless communications

This paper examines some of the most promising and challenging scenarios in IoT, and shows why current compute and storage models confined to data centers will not be able to meet the requirements of many of the applications foreseen for those scenarios. Our analysis is particularly centered on three interrelated requirements: 1) mobility; 2) reliable control and actuation; and 3) scalability, especially, in IoT scenarios that span large geographical areas and require real-time decisions based on data analytics. Based on our analysis, we expose the reasons why Fog Computing is the natural platform for IoT, and discuss the unavoidable interplay of the Fog and the Cloud in the coming years. In the process, we review some of the technologies that will require considerable advances in order to support the applications that the IoT market will demand.

/pdf/key-ingredients-in-an-iot-recipe-fog-computing-cloud-1hcgka7ea5.pdf

Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing

A multi-streaming processor (14) has multiple streams (4) for processing multiple threads (1-3), and an instruction scheduler (5) including a priority record of priority codes (7, 8) for one or more of the streams (4). The priority codes (7, 8) determine in some embodiments relative access to resources (10-13) as well as which stream (4) has access at any point in time. In other embodiments priorities are determined dynamically and altered on-the-fly, which may be done by various criteria, such as on-chip processing statistics, by executing one or more priority algorithms, by input from off-chip (9), according to stream loading, or by combinations of these and other methods. In one embodiment a special code is used for disabling a stream, and streams may be enabled and disabled dynamically by various methods, such as by on-chip events, processing statistics, input from off-chip, and by processor interrupts. Some specific applications are taught, including for IP-routers and digital signal processors.

Prioritized instruction scheduling for multi-streaming processors

A multi-streaming processor has a plurality of streams for streaming one or more instruction threads, a set of functional resources for processing instructions from streams; and interstream control mechanisms whereby any stream may effect the operation of any other stream. In various embodiments the interstream control mechanisms include mechanisms for accomplishing one or more of enabling or disabling another stream, putting another stream into a sleep mode or awakening another stream from a sleep mode, setting priorities for another stream relative to access to functional resources, and granting blocking access by another stream to functional resources. A Master Mode is taught, wherein one stream is granted master status, and thereby may exert any and all available control mechanisms relative to other streams without interference by any stream. Supervisory modes are taught as well, wherein control may be granted from minimal to full control, with compliance of controlled streams, which may alter or withdraw control privileges. Various mechanisms are disclosed, including a mechanism wherein master status and interstream control hierarchy is recorded and amended by at least one on-chip bit map. In this mechanism each stream maintains and edits a bitmap granting or withdrawing control privileges for each other stream, the settings valid for any stream but a Master stream, which will ignore the settings.

Interstream control and communications for multi-streaming digital processors

Page-based virtual memory improves programmer productivity, security, and memory utilization, but incurs performance overheads due to costly page table walks after TLB misses. This overhead can reach 50% for modern workloads that access increasingly vast memory with stagnating TLB sizes. To reduce the overhead of virtual memory, this paper proposes Redundant Memory Mappings (RMM), which leverage ranges of pages and provides an efficient, alternative representation of many virtual-to-physical mappings. We define a range be a subset of process's pages that are virtually and physically contiguous. RMM translates each range with a single range table entry, enabling a modest number of entries to translate most of the process's address space. RMM operates in parallel with standard paging and uses a software range table and hardware range TLB with arbitrarily large reach. We modify the operating system to automatically detect ranges and to increase their likelihood with eager page allocation. RMM is thus transparent to applications. We prototype RMM software in Linux and emulate the hardware. RMM performs substantially better than paging alone and huge pages, and improves a wider variety of workloads than direct segments (one range per program), reducing the overhead of virtual memory to less than 1% on average.

/pdf/redundant-memory-mappings-for-fast-access-to-large-memories-1tuiiebp5u.pdf

Redundant memory mappings for fast access to large memories

Current trends in microprocessor architecture design are leading towards a dramatic increase of core-level parallelization, wherein a given number of independent processors or cores are interconnected. Since the main bottleneck is foreseen to migrate from computation to communication, efficient and scalable means of inter-core communication are crucial for guaranteeing steady performance improvements in many-core processors. As the number of cores grows, it remains unclear whether initial proposals, such as the Network-on-Chip (NoC) paradigm, will meet the stringent requirements of this scenario. This position paper presents a new research area where massive multicore architectures have wireless communication capabilities at the core level. This goal is feasible by using graphene-based planar antennas, which can radiate signals at the Terahertz band while utilizing lower chip area than its metallic counterparts. To the best of our knowledge, this is the first work that discusses the utilization of graphene-enabled wireless communication for massive multicore processors. Such wireless systems enable broadcasting, multicasting, all-to-all communication, as well as significantly reduce many of the issues present in massively multicore environments, such as data coherency, consistency, synchronization and communication problems. Several open research challenges are pointed out related to implementation, communications and multicore architectures, which pave the way for future research in this multidisciplinary area.

/pdf/graphene-enabled-wireless-communication-for-massive-40bwyj53wo.pdf

Mario Nemirovsky

Papers

Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing

Prioritized instruction scheduling for multi-streaming processors

Interstream control and communications for multi-streaming digital processors

Redundant memory mappings for fast access to large memories

Graphene-enabled wireless communication for massive multicore architectures